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  • RAG or Fine-Tuning? A Clear Guide to Using Both

    RAG or Fine-Tuning? A Clear Guide to Using Both

    In the rush to implement AI across organizational operations, one must strike a balance between adaptability and accuracy. Should you rely on retrieval-based intelligence to maintain agility, or should you hardwire experience into the model to ensure precision?

    This is a strategic decision, and making the right call at the right time can determine the success of everything from automated policy interpretation to conversational AI. Both offer paths to smarter AI; however, they serve different needs, and selecting the wrong one can be the difference between insight and illusion.

    RAG: Fast, Flexible, and Context-Aware

    Retrieval-Augmented Generation (RAG) is where most organizations begin their journey. Instead of retraining an LLM, RAG enhances its responses by pulling real-time context from a vector database. Here’s how it works:

    1. Vector Encoding: Your documents or knowledge base are embedded into a vector store.
    2. Prompt Engineering: At inference time, the user’s query triggers a semantic search.
    3. Dynamic Injection: Relevant documents are retrieved and included in the prompt.
    4. LLM Response: The model uses this injected context to generate a grounded, informed response.

    This process is compute-efficient, versionless, and ideal when knowledge is fluid or frequently updated, such as government policies, IoT feeds, or legal frameworks.

    Where Does RAG End?

    While RAG excels at injecting facts, it has limitations:

    • It can’t teach the model how to reason.
    • It doesn’t enforce stylistic consistency.
    • And when retrieval fails, hallucinations creep in.

    That’s your cue: when structure, tone, or deterministic behavior become priorities or when retrieved content isn’t enough to answer correctly, you transition to fine-tuning.

    Enter Fine-Tuning: Precision with Permanence

    Fine-tuning involves retraining the base model on your domain-specific data, embedding domain-specific language, decision logic, and formatting directly into its parameters.

    This is essential when:

    • You want consistent behavioral patterns (e.g., legal summaries, medical reports).
    • You need high accuracy where the retrieval is partially optimal or completely absent.
    • Your workflows involve fixed taxonomies or templates.
    • Hallucination pt.

    Fine-tuning embeds knowledge deep into the model for deterministic output.

    Build Both With DataNeuron Without Building Infrastructure

    Unlike fragmented ML stacks, DataNeuron lets you orchestrate RAG and fine-tuning in a single interface. Most platforms force teams to juggle disconnected tools just to get a basic RAG or fine-tuning pipeline running. DataNeuron changes that.

    • Unified no-code interface to design, chain, and orchestrate both RAG and fine-tuning workflows without DevOps dependency
    • DSEAL powered Dataset Curation to automatically generate high-quality, diverse datasets, structured and ready for fine-tuning with minimal manual prep
    • Built-in prompt design tools to help structure and adapt inputs for both generation and retrieval use cases
    • Robust evaluation system that supports multi-layered, continuous testing spanning BLEU/ROUGE scoring, hallucination tracking, and relevance validation, ensuring quality improves over time
    • Versioned model tracking and performance comparison across iterations, helping teams refine workflows based on clear, measurable outcomes

    Use DataNeuron to monitor and iterate across both workflows. 

    1. Fine-tune the LLM for tone, structure, and in-domain reasoning.
    2. Layer in RAG to supply the most recent facts or data points.

    This hybrid pattern ensures that your AI communicates reliably and stays up to date.

    These metrics help ensure both your fine-tuned and RAG-based pipelines stay grounded, efficient, and aligned with real-world expectations.

    Start Smart with DataNeuron

    • A customer support team used fine-tuning on 10,000 Q&A pairs and cut error rates by 40%.
    • A public sector client layered RAG into live deployments across 50+ policies, with no retraining needed.

    Both teams used the same platform. One interface. Multiple workflows. Wherever you are in your AI journey, DataNeuron gets you moving quickly.

  • A2A:  The Rulebook Governing Multi-Agent Collaboration

    A2A:  The Rulebook Governing Multi-Agent Collaboration

    If the internet allowed everyone to send data, but there were no rules (like HTTP, TCP/IP, or DNS) on how to format, interpret, or verify it. One site would send text as images, another as binary, and another with no headers. You could connect, but you’d rarely understand what was sent. That’s what MAS looks like without A2A (Agent-to-Agent Protocol). 

    The Model Context Protocol (MCP) gives multi-agent systems (MAS) a shared communication channel. A2A provides the contractual rules of interaction, making them reliable enough for enterprise and cross-organizational use.

    Why Multi-Agent Systems Struggle Without A2A

    Even with a strong communication layer (MCP), MAS still face critical shortcomings when there’s no governing protocol like A2A:

    • Ambiguity of meaning
    • Lack of trust 
    • Security vulnerabilities 
    • Compliance gaps
    • Cross-boundary failures 

    In short, without A2A, multi-agent systems remain prone to misalignment and unsuitable for real-world enterprise environments.

    How A2A Works

    A2A operates through a set of principles that bring clarity and governance to MAS:

    1. Structured Messages
       Every message comes with a strict schema with defined types, context, and intent, so ambiguity is removed.
    2. Authentication & Trust
       Messages can be cryptographically signed, allowing agents to verify the sender’s identity and authority.
    3. Validation Rules
      Before acting, agents validate whether a message conforms to agreed-upon standards.
    4. Governance Layer
       A2A encodes rules of interaction: who can do what, under what conditions, and with what accountability.
    5. Cross-Boundary Collaboration
       Agents across organizations or domains can work together without being tightly coupled, thanks to standardized contracts.

    A2A in Action: 

    With A2A, every step is standardized, signed, and auditable. 

    By building A2A into our platform, we ensure that agent-to-agent communication isn’t just possible, but governed and reliable. This approach helps organizations:

    • Operate multi-department workflows with confidence.
    • Collaborate securely with external vendors’ agents.
    • Maintain compliance without adding manual oversight.

    Our mission is to make MAS not only intelligent but also accountable. A2A is the step that makes that possible.

    Why A2A Shapes the Future of Agentic AI

    Looking ahead, we believe A2A will define how agent ecosystems evolve in three key ways:

    1. Governed Autonomy
       Agents won’t just act independently; they’ll act within enforceable rules and standards.
    2. Cross-Organizational Collaboration
       As businesses connect agents across ecosystems, A2A will be the “link language” that ensures safe cooperation.
    3. Trusted Intelligence
       Enterprises will demand explainable, auditable AI- A2A provides the contractual layer to deliver it.

    At DataNeuron, we move toward ecosystems of interoperable agents, and we believe A2A will be the reason they can do so with confidence.

  • The Agentic AI Toolbook: Smarter Tools for Smarter Outcomes

    The Agentic AI Toolbook: Smarter Tools for Smarter Outcomes

    For years, enterprise AI conversations have revolved around agents. The autonomous entities that plan, reason, and act. In slide decks and product pitches, the agent is portrayed as a brain: it processes inputs, makes decisions, and produces outputs. But when you peel back the layers of a real system, a different story emerges. The agent is only as powerful as the tools it can call.

    The new Agentic AI systems are expected not only to reason but also to execute. Before we talk about tools, let’s clarify what an agent really is and why, at DataNeuron, we believe the toolbook deserves just as much attention as the agent itself.

    What an Agent Really Does

    An agent handles the thinking and decision-making, while tools handle the doing. Tools perform the actual actions, such as classifying text, scraping websites, sending emails, pulling data from CRMs, or writing into dashboards. Without tools, an agent can process information but can’t take action. In short, the agent decides what needs to be done and when.

    From Reasoning to Action

    This is where the execution layer comes in. Tools translate an agent’s intent into real-world action. Crucially, the agent doesn’t have to know how each tool works internally; it only needs to know three things:

    What the tool does

    What input to give it

    What output to expect

    This clean separation of reasoning (agent) and execution (tools) keeps systems modular, interpretable, and easy to govern. You can upgrade or swap out tools without retraining the agent, catering to what large enterprises need: faster iteration cycles and safer deployments.

    A Quick Scenario–Customer Support

    Suppose your AI receives the task “analyze complaints and send a summary to the team.” A traditional chatbot would try to handle everything within a single model. An agentic system built on DataNeuron does it differently:

    • Fetches customer history from the CRM using an API-based tool.
    • Classifies the complaint and extracts order IDs using DataNeuron Native Tools, such as multiclass classifiers and NER.
    • Retrieves troubleshooting steps via Structured RAG.
    • Summarizes the case with a custom tool configured by your support ops team.
    • Sends an acknowledgment using an external mail connector.

    The result is an automated pipeline that used to require manual coordination across multiple teams.

    Inside the DataNeuron Toolbook

    At DataNeuron, we built the Toolbook to make this orchestration simple and scalable. Instead of hand-coding workflows, users can select from a library of pre-built tools or define their own. Everything is callable through standard input/output schemas so that the agent can pick and mix tools without brittle integrations.

    We organize our toolbook into four pillars, each extending the agent’s reach differently.

    1. DataNeuron Native Tools

    These are our first creation in studio-high-utility, pre-configured tools optimized for AI workflows, often known as the “intelligence primitives” of your agent. They’re ready to call as soon as you deploy an agent:

    • Structured RAG (Retrieval-Augmented Generation): Combines document indexing with structured memory, letting agents pull curated data sets in real time. Ideal for regulatory documents, knowledge bases, or customer support manuals.
    • Contextual Search: Allows agents to query within a bounded knowledge base, perfect for domain-specific applications like legal, customer service, or biomedical agents.
    • Multiclass & Multilabel Classifiers: Let agents tag or categorize inputs, such as sorting customer feedback by sentiment and urgency or routing tickets to the right department.
    • Named Entity Recognition (NER): Extracts names, locations, products, and other entities, essential for parsing resumes, contracts, or customer emails.

    You don’t code these tools; you configure them. The agent calls them as needed, with predictable inputs and outputs.

    2. External Tools

    These extend the agent’s reach into the broader digital ecosystem. Think of them as bridges between your agent and the open web or third-party services. Examples include:

    • Web Scraper to pull structured data from webpages, prices, job postings, and event schedules.
    • Google, Wikipedia, and Arxiv Search for real-time knowledge retrieval, essential for summarizing or validating claims.
    • Mail Sender to automate communications, acknowledgments, follow-ups, and onboarding instructions.

    With external tools, your agent can enrich its answers, validate facts, and trigger outward-facing actions.

    3. Custom Tools

    Not every enterprise workflow fits into an off-the-shelf template. That’s why we let you create custom tools by simply defining:

    • name (e.g., “SummarizeComplaint”)
    • description (“Summarizes customer complaint emails into action items”)
    • input/output schema

    Based on this metadata, the DataNeuron platform generates a callable tool automatically. This is especially powerful in domains where business logic is unique, such as parsing health insurance claims, configuring automated compliance checks, or running internal analytics.

    You define what the tool does, not how it does it, while the system handles the integration.

    4. API-Based Tools

    These connect agents to external systems or databases, turning your AI from a smart assistant into an operational actor. You define the tool’s:

    • Name and purpose
    • API endpoint and method
    • Auth/token structure
    • Request/response format

    From there, the platform generates a tool that the agent can call. This enables workflows like:

    • Fetching real-time data from a food delivery backend.
    • Pushing recommendations into a CRM.
    • Triggering marketing campaigns.

    API-based tools let agents interact with your production systems securely and at scale.

    Let’s consider another scenario of a Digital Health Assistant

    To see how these pieces fit together, imagine a hospital deploying a digital health assistant for its doctors. A patient logs in and requests an explanation of their latest blood test report:

    • API-Based Tool fetches the patient’s lab results from the hospital’s CRM or EHR database.
    • DataNeuron Native Tools (NER + multilabel classifier + Structured RAG) extract key metrics, flag abnormal values, and pull relevant medical guidelines from an internal knowledge base.
    • Custom Tool created by the hospital’s analytics team generates a plain-language summary of the patient’s health status and next steps.
    • External Tools email the report to the patient and physician, and optionally pull the latest research articles to confirm if the doctor requests supporting evidence.

    All of this happens automatically. The agent decides the sequence of actions; each tool performs its specific function. Data is fetched, analyzed, explained, enriched with context, and delivered without the doctor or patient stitching the pieces together manually.

    Why This Matters?

    Moving from model-first to tool-first thinking turns AI from a smart assistant into an operational actor. Modular tools let agents take sequential actions toward complex goals while giving enterprises governance and flexibility: tools can be audited or swapped without altering the agent’s logic, new capabilities can be added like apps on a phone, and clear input/output schemas simplify security and compliance integration.

    The most valuable AI tool in the future won’t be the one that “knows” everything. It will be the one that knows how to get things done, and that’s exactly what the DataNeuron Agentic AI Toolbook is built for.

    At DataNeuron, we’re not trying to replace engineers, but giving them a new medium. Workflows can be designed using reusable tools, customized by intent, and executed by agents who know when and why to use them. Instead of one massive, brittle model, you get a living ecosystem where each component can evolve independently.

  • MCP: The Communication Backbone of Multi-Agent Systems

    MCP: The Communication Backbone of Multi-Agent Systems

    AI progress has been upgraded with larger models, more parameters, and bigger datasets. This created powerful Large Language Models (LLMs), but exposed their limits: even the best models falter in multi-domain workflows, hallucinate facts, lose context, and struggle with complex coordination.

    Multi-Agent Systems (MAS) emerged to address these gaps by deploying specialized agents for tasks like summarization, search, compliance checking, and analysis. Together, they can outperform a single model but only if they work coherently. In enterprise customer support, for example, one agent may retrieve knowledge, another analyze sentiment, and a third draft a reply. Without shared context, they duplicate work, contradict each other, or miss critical data.

    The Model Context Protocol (MCP) closes this gap. It standardizes how agents exchange state, intent, and outputs, turning isolated components into a coordinated, auditable system capable of reliable multi-step outcomes at scale.

    Why Current Multi-Agent Systems Fall Short

    Before understanding MCP, let’s look at what MAS misses without it:

    Today’s MAS often acts like loosely coupled tools rather than a synchronized team. The result is unpredictability, an unacceptable outcome for enterprise use cases where accuracy, compliance, and auditability matter.

    MCP: A Protocol Born of Necessity

    The Model Context Protocol (MCP) is a standardized communication framework that enables agents in a multi-agent system to “speak the same language.” Acting as both a universal translator and a message bus, MCP lets any agent, whether an LLM, retrieval engine, API connector, or compliance checker, exchange context reliably and consistently.

    How MCP Works

    At its core, MCP provides five foundational capabilities:

    • Standardized Messaging
    • Shared Memory Access
    • Publish/Subscribe Coordination
    • Dynamic Composi tion 
    • Medium-Agnostic Transport

    How would this work in Financial Compliance?

    Consider a bank’s compliance workflow:

    One agent ingests regulatory documents.

    Another checks transactions against relevant rules.

    A third summarizes the findings for auditors.

      With MCP, the pipeline is traceable, resilient, and composable: each agent publishes standardized outputs into a shared context, while downstream agents subscribe and act on verified data.

      MCP in Action at DataNeuron

      At DataNeuron, MCP is treated as the connective tissue of intelligent automation. MCP lets them expose functionality via an HTTP server, a studio server, or a custom API and register it under the MCP schema. From that moment, MCP handles orchestration: routing intent, synchronizing state, and coordinating workflows.

      This design allows us to:

      Integrate LLMs with retrieval engines and domain-specific APIs seamlessly.

      Orchestrate cross-departmental workflows without losing auditability.

      Scale agent ecosystems without creating central bottlenecks.

      By formalizing how agents communicate and share context, MCP converts fragmented tools into a unified, auditable, and scalable multi-agent system ready for real-world deployment.

      Why MCP Is Foundational to the Next Wave of Agentic AI

      Enterprise AI is moving away from monolithic, one-size-fits-all models toward modular, composable systems. In this new architecture, the MCP functions as the critical communication backbone, allowing intelligent agents to coordinate, adapt, and scale reliably.

      By standardizing how context, state, and intent flow between agents, MCP lays the groundwork for future-proof AI ecosystems. Three shifts illustrate this impact:

      Composable Intelligence

      Governed Autonomy 

      Cross-Ecosystem Interoperability 

      Taken together, these shifts position MCP as a cornerstone of scalable, auditable, and future-ready multi-agent systems. MCP is the infrastructure layer that enables businesses to design AI workflows that are as dynamic and trustworthy as the environments in which they operate.

    1. Beyond the “Looks Good to Me”: Why LLM Evals Are Your New Best Friend

      Beyond the “Looks Good to Me”: Why LLM Evals Are Your New Best Friend

      As large language models transition from lab experiments to real-world applications, the way we evaluate their performance must evolve. A casual thumbs-up after scanning a few outputs might be fine for a weekend project, but it doesn’t scale when users depend on models for accuracy, fairness, and reliability.

      LLM evaluations or evals do this job for you. They turn subjective impressions into structured, repeatable measurements. More precisely, evals transform the development process from intuition-driven tinkering into evidence-driven engineering, a shift that’s essential if we want LLMs to be more than just impressive demos.

      The Eval-Driven Development Cycle: Train, Evaluate, Repeat 

      At DataNeuron, evaluation (Eval) is the core of our fine-tuning process. We follow a 5-step, iterative loop designed to deliver smarter, domain-aligned models:

      1. Raw Docs

      The process starts with task definition. Whether you’re building a model for summarization, classification, or content generation, we first collect raw, real-world data, i.e., support tickets, reviews, emails, and chats, directly from your business context.

      2. Curated Evals

      We build specialized evaluation datasets distinct from the training data. These datasets are crafted to test specific capabilities using diverse prompts, edge cases, and real-world scenarios, ensuring relevance and rigor.

      3. LLM Fine-Tune

      We fine-tune your model (LLaMA, Mistral, Gemma, etc.) using task-appropriate data and lightweight methods like PEFT or DPO, built for efficiency and performance.

      4. Eval Results

      We evaluate your model using curated prompts and subjective metrics like BLEU, ROUGE, and hallucination rate, tracking not just what the model generates, but how well it aligns with intended outcomes.

      5. Refinement Loop

      Based on eval feedback, we iterate, refining datasets, tweaking parameters, or rethinking the approach. This cycle continues until results meet your performance goals.

      Evals guide you towards better models by providing objective feedback at each stage, ensuring a more intelligent and efficient development cycle. So, what exactly goes into a robust LLM evaluation framework?

      Core Components of a Robust LLM Evaluation Framework

      Human Validation

      We recognize the invaluable role of human expertise in establishing accurate benchmarks. Our workflow enables the generation of multiple potential responses for a given prompt. Human validators then meticulously select the response that best aligns with the desired criteria. This human-approved selection serves as the definitive “gold standard” for our evaluations.

      Prompt Variations

      DataNeuron empowers users to define specific “eval contexts” and create diverse variations of prompts. This capability ensures that your model is rigorously evaluated across a broad spectrum of inputs, thereby thoroughly testing its robustness and generalization capabilities.

      Auto Tracking

      Our evaluation module automatically compares the responses generated by your fine-tuned model against the human-validated “gold standard.” This automated comparison facilitates the precise calculation of accuracy metrics and allows for the consistent tracking of how well your model aligns with human preferences. The fundamental principle here is that effective fine-tuning should lead the model to progressively generate responses that closely match those previously selected by human validators.

      Configurable Pipelines

      We prioritize flexibility and control. DataNeuron’s entire evaluation process is highly configurable, providing you with comprehensive command over every stage from data preprocessing and prompt generation to the selection of specific evaluation metrics.

      DataNeuron: Your Partner in Eval-Driven Fine-Tuning

      At DataNeuron, we’re building a comprehensive ecosystem to streamline your LLM journey, and Evals are a central piece of that puzzle. While we’re constantly evolving, here’s a glimpse of how DataNeuron empowers you with eval-driven fine-tuning:

      Core Tenets of DataNeuron’s Evaluation Methodology

      Human Validation:

      We recognize the invaluable role of human expertise in establishing accurate benchmarks. Our workflow enables the generation of multiple potential responses for a given prompt. Human validators then meticulously select the response that best aligns with the desired criteria. This human-approved selection serves as the definitive “gold standard” for our evaluations.

      Prompt Variations:

      DataNeuron empowers users to define specific “eval contexts” and create diverse variations of prompts. This capability ensures that your model is rigorously evaluated across a broad spectrum of inputs, thereby thoroughly testing its robustness and generalization capabilities.

      Auto Tracking:

      Our evaluation module automatically compares the responses generated by your fine-tuned model against the human-validated “gold standard.” This automated comparison facilitates the precise calculation of accuracy metrics and allows for the consistent tracking of how well your model aligns with human preferences. The fundamental principle here is that effective fine-tuning should lead the model to progressively generate responses that closely match those previously selected by human validators.

      Configurable Pipelines:

      We prioritize flexibility and control. DataNeuron’s entire evaluation process is highly configurable, providing you with comprehensive command over every stage from data preprocessing and prompt generation to the selection of specific evaluation metrics.

      Best Practices & Avoiding the Potholes

      Here are some hard-earned lessons to keep in mind when implementing eval-driven fine-tuning:

      Don’t Overfit to the Eval:

      Just like you can overfit your model to the training data, you can also overfit to your evaluation set. To avoid this, diversify your evaluation metrics and periodically refresh your test sets with new, unseen data.

      Beware of Eval Drift:

      The real-world data your model encounters can change over time. Ensure your evaluation datasets remain representative of this evolving reality by periodically updating them.

      Balance Latency and Quality:

      Fine-tuning can sometimes impact the inference speed of your model. Carefully consider the trade-off between improved quality and potential increases in latency, especially if your application has strict performance SLAs.

      With its focus on structured workflows and integration, DataNeuron urges users to build more reliable and effective LLM-powered applications. Moving beyond subjective assessments is crucial for unlocking the full potential of LLM fine-tuning. Evals provide the objective, data-driven insights you need to build high-performing, reliable models.

      At DataNeuron, we’re committed to making this process seamless and accessible, empowering you to fine-tune your LLMs and achieve remarkable results confidently.

    2. DataNeuron Feature Catalogue

      DataNeuron Feature Catalogue

      The DataNeuron Pipeline

      DataNeuron helps you accelerate and automate human-in-loop annotation for developing AI solutions. Powered by a data-centric platform, we automate data labeling, the creation of models, and end-to-end lifecycle management of ML.

      Ingest

      Upload Visualization

      Users can upload the entire data available with them without performing any filteration to remove out of scope paragraphs.

      The data can be uploaded in various file formats supported by the platform.

      The platform has an in-built feature that can handle out-of-scope paragraphs and separate them from the classification data. This functionality is optional and can be toggled on or off anytime during the process.

      Structure

      Structure Visualization

      The next step is the creation of the project structure.

      Instead of a simple flat structure with just the classes defined, we provide the user with the option to create a multi-level (hierarchical) structure so that he can extract the data grouped into domains, subdomains, and indefinitely continue dividing into further subparts depending on his needs.

      Any of the defined nodes can be marked as a class for the data to be classified into irrespective of the level on which it is in the hierarchy. This provides flexibility to create any level of ontology for classification.

      Validate

      Validate Visualization

      User does not have to go through the entire dataset to sort out paragraphs that belong to a certain class and label them to provide training data for the model, which can be a tedious and difficult task.

      We propose a validation based approach:

      • The platform provides the users with suggestions of paragraphs that are most likely to belong to a certain category/class based on an efficient context-based filtering criterion.
      • The user simply has to validate the suggestions, i.e, check whether or not the suggested class is correct.

      This reduces the effort put in by the user in filtering out the paragraphs belonging to a category from the entire dataset by a large margin.

      The strategic annotation technique allows the user to adopt a ‘one-vs-all’ strategy, which makes the task far easier than having to take into consideration all the defined classes, which can be a large number depending on the problem at hand, while tagging a paragraph.

      Our intelligent filtering algorithm ensures “edge-case” paragraphs, i.e paragraphs that do not have obvious correlation with a class but still belong to that class, are not left out.

      This step is broken down into 2 stages:

      • The validation done by the user in the first stage is used for determining the annotation suggestions offered in the second stage.
      • Each batch of annotation is used to improve the accuracy of the filtering algorithm for the next batch.

      The platform also provide a summary screen after each batch of validation which provides the user with an idea as to many more paragraphs he might need to validate per class in order to achieve a higher accuracy.

      It also helps determine when to stop the validation for a specific class and focus more on a class for which the platform projects low confidence.

      Train

      Train Visualization

      User invests virtually no effort into the model training step and the model training can be initiated with the simple click of a button.

      The complete training process is automatic which performs preprocessing, feature engineering, model selection, model training, optimization and k-fold validation.

      After the final model is trained, the platform shows a detailed report of the trained model is presented to the user which includes the overall accuracy of the model as well as the accuracy achieved per class.

      Iterate

      Iterate Visualisation

      Once the model has been trained, we provide the user with 2 options:

      • Continue to the deployment stage if the trained model matches their expectations.
      • If the model does not achieve the desired results, the user can choose to go back and provide more training paragraphs (by validating more paragraphs or uploading seed paragraphs) or alter the project structure to remove some classes and then retrain the model to achieve better results.

      Deploy (“No-Code” Prediction Service)

      Deploy Visualisation

      Apart from providing the final annotations on the data uploaded by the user using the trained model, we also provide a prediction service which can be used to make a prediction on any new paragraphs in exchange for a very minimal fee.

      This does not require any knowledge of coding and users can utilize this service for any input data from the platform.

      This can also be integrated into other platforms by making use of the exposed prediction API or the deployed Python package.

      No Requirement for a Data Science/Machine Learning Expert

      The DataNeuron ALP is designed in such a way that no prerequisite knowledge of data science or machine learning is required to utilize the platform to its maximum potential.

      For some very specific use cases, a Subject Matter Expert might be required but for the majority of use cases, an SME is not required in the DataNeuron Pipeline.

    3. DataNeuron vs Human in the Loop — ROI Calculator

      DataNeuron vs Human in the Loop — ROI Calculator

      Experiment

      We run the numbers on conventional Data Annotation projects to gauge the ROI that can be generated through the DataNeuron platform.

      Conventional Human in the Loop

      Time required for one user to annotate 100,000 paragraphs = 1000 hours (range: 500–1500 hours)

      DataNeuron + Human in the Loop

      The number of paragraphs that require validation on the DataNeuron platform is 6000 (Range: 4500–9000 paras)

      Time required for 1 user to annotate 6000 paragraphs is 50 hours (range: 40–60 hours)

      Conclusion

      ROI = ((total_in_house_team_cost-Total_Dataneuron_ALP_cost)/Total_Dataneuron_ALP_cost)*100

((10000-1350)/1350)*100 = 640.74

      ROI: 640%

    4. How well does DataNeuron handle the Tax & Legal Use Case?

      How well does DataNeuron handle the Tax & Legal Use Case?

      This is the table that explains the dataset that was used to conduct this case study.

      Explaining the DataNeuron Pipeline

      This is the DataNeuron Pipeline. Ingest, Structure, Validate, Train, Predict, Deploy and Iterate.

      Results of our Experiment

      Results of our Experiment

      Reduction in SME Labelling Effort

      During an in-house project, the SMEs have to go through all the paragraphs present in the dataset in order to figure out which paragraphs actually belong to the 73 classes mentioned above. This would usually take a tremendous amount of time and effort.

      When using DataNeuron ALP, the algorithm was able to perform strategic annotation on 15000 raw paragraphs and filter out the paragraphs that belonged to the 73 classes and provide 4303 paragraphs to the user for validation. Taking as little as 45 seconds to annotate each paragraph, an in-house project would take an estimate of 187.5 hours just to annotate all the paragraphs while by using DataNeuron, it only took 35.85 hours.

      Difference in paragraphs annotated between an in-house solution and DataNeuron.

      Advantage of Suggestion-Based Annotation

      Instead of making users go through the entire dataset to label paragraphs that belong to a certain class, DataNeuron uses a validation-based approach to make the model training process considerably easier. The platform provides the users with a list of annotated/ labelled paragraphs that are most likely to belong to the same class by using context-based filtering and analysing the masterlist. The users simply have to validate whether the system labelled paragraph belongs to the class mentioned. This validation-based approach also reduces the time it takes to annotate each paragraph. Based on our estimate, it takes approximately 30 seconds for a user to identify whether a paragraph belongs to a particular class. Based on this, it would take an estimate of 35.85 hours for the users to validate 4303 paragraphs provided by the DataNeuron ALP. When compared to the 187.5 hours it would take for an in-house team to complete the annotation process, DataNeuron offers a staggering 81% reduction in time spent.

      Difference in time spent annotating between an in-house solution and DataNeuron.

      The Accuracy Achieved

      When conducting this case study, the accuracy we achieved for the model trained by the DataNeuron ALP was 87% which, considering the high number of classes and small number of training paragraphs, proves to work very well in real world scenarios. The accuracy of the model trained by the DataNeuron ALP can be improved by validating more paragraphs or by adding seed paragraphs.

      Calculating the Cost ROI

      The number of paragraphs that needs to be annotated in an in-house project is 15000 and it would cost approximately $3280. The number of paragraphs that needs to be annotated when using the DataNeuron ALP is 4303 since most of the paragraphs which did not belong to any of 73 classes were discarded using context-based filtering. The cost for annotating 4303 paragraphs using the DataNeuron ALP is $976.85.

      Difference in cost between an in-house solution and DataNeuron.

      No Requirement for a Data Science/Machine Learning Expert

      The DataNeuron ALP is designed in such a way that no prerequisite knowledge of data science or machine learning is required to utilize the platform to its maximum potential.

      For some very specific use cases, a Subject Matter Expert might be required but for the majority of use cases, an SME is not required in the DataNeuron Pipeline.

    5. Announcing The New DataNeuron Platform: Redefining Data Labelling through Automation for the AI-First World

      Announcing The New DataNeuron Platform: Redefining Data Labelling through Automation for the AI-First World

      DataNeuron is thrilled to announce the official launch of the DataNeuron Automated Learning Platform (ALP). The ALP has been strategically designed to accelerate and automate human-in-loop annotation for developing AI solutions. Powered by a data-centric platform, we automate data labeling, the creation of models, and end-to-end lifecycle management of ML.

      We are a team of Data Science enthusiasts having first-hand experience of dealing with data analysts, subject matter experts and data scientists to fulfil the labelled data requirements for building highly accurate contextual algorithms for various use-cases. Our aim is to accelerate the development and provide better explainability of AI.

      We are also excited to partner with leading venture capitalists, angel investors and strategic advisors in expanding the horizons of DataNeuron.

      But why should we switch from human labelling to the DataNeuron ALP? That’s a great question! Based on our findings from the case studies we have conducted, we have found out that using the DataNeuron ALP can reduce the time spent in annotating by a staggering 89.10%, reduce the number of paragraphs validated by 83.55%, reduce the cost expenditure by 77.83% and yield an ROI of an astounding 372.22%.

      The DataNeuron Pipeline

      Those numbers sound promising but what more can we do on the DataNeuron ALP? Once Again, that’s a great question! Apart from getting accurately labelled data, the DataNeuron ALP can be used to perform no-code prediction. With just a click of a button, the platform can be used to make a prediction on any new paragraphs in exchange for a very minimal fee. This does not require any knowledge of programming and users can utilize this service for any input data from the platform. This can also be integrated into other platforms by making use of the exposed prediction API or the deployed Python package.

      As a cherry on top, the DataNeuron ALP is designed in such a way that no prerequisite knowledge of data science or machine learning is required to utilize the platform to its maximum potential. The users only need some knowledge of the domain they are working on and the details of the project and they’re good to go! For some very specific use cases, a Subject Matter Expert might be required but for the majority of use cases, an SME is not required in the DataNeuron Pipeline.

    6. Distill & Deploy: Scalable LLMs Made Simple With DataNeuron 

      Distill & Deploy: Scalable LLMs Made Simple With DataNeuron 

      Large Language Models (LLMs) like Llama and Mistral offer immense potential, but their massive size creates deployment challenges, as slow speeds and hefty operational costs hinder their real-world applications. When building a real-time application for your enterprise or aiming for budget deployment at scale, running a 13 B+ parameter model is impractical.

      This is where model distillation comes into play.

      Think of it as extracting the core wisdom of a highly knowledgeable “teacher” model and transferring it to a smaller, more agile “student” model. At DataNeuron, we’re revolutionizing this process with our LLM Studio. Our platform boasts a smooth workflow that integrates intelligent data curation with a powerful distillation engine that delivers:

      • Up to 10X faster inference speed*
      • 90% reduction in model size*
      • Significant cost 
      • Saving on GPU infrastructure
      • High accuracy retention

      Why is Distillation a Game Changer?

      Deploying billion-parameter LLMs to production introduces four major bottlenecks:

      1. Latency: A few seconds of latency to produce responses from big models is not suitable for real-time use in conversational AI, customer, and real-time interactions
      2. Infrastructure Cost: LLMs are GPU-intensive. Executing one inference on a +13B model doesn’t sound like much until you are dealing with thousands of simultaneous users. Your cloud expenses surge quickly. A 13B parameter model might end up costing 5X more to execute than a distilled 2B version.
      3. Infrastructure Demand: Scaling mass models necessitates more powerful GPUs, scaled serving infrastructure, and continuous performance tuning. Deployment on devices becomes infeasible when model sizes exceed 5B parameters.
      4. Hallucinations: Larger models are more likely to produce inapt or irrelevant answers without proper tuning.

      Model distillation removes these limitations by transferring the “knowledge” from a large (teacher) model (e.g., Llama 13B) to a smaller (student) model (e.g., a Llama 1B), retaining performance but vastly improving efficiency. 

      Navigating the Pitfalls of Traditional Distillation

      Traditional model distillation trains a smaller “student” model to mimic a larger “teacher” by matching their outputs. While conceptually simple, valuable distillation is complex, involving careful data selection, proper loss functions (typically based on the teacher’s probability distributions for richer information transfer), and iterative testing with hyperparameters. For example, distilling a large language model for mobile deployment involves training a smaller model on relevant text, possibly incorporating the teacher’s predicted word probabilities to capture style variations.

      Without the right tools and technology to manage this complexity, the process can be time-consuming, error-prone, and difficult to scale, limiting the practical implementation of this efficiency-boosting technique.

      How is DataNeuron Doing Things Differently?

      LLM Studio allows you to easily design and manage lightweight, powerful models as per your needs. Our approach promotes intelligent data curation as the foundation for successful information transfer.

      Here’s how we streamline the process: 

      1. Data Selection with Divisive Sampling (D-SEAL) 

      We deploy our proprietary Divisive Sampling (D-SEAL) system to choose the most informative training data. D-SEAL groups comparable data points, ensuring that your student model learns from a diverse range of examples relevant to its target domain. This curated dataset, potentially built using prompts and responses generated by Retrieval-Augmented Generation (RAG), serves as the bedrock for effective distillation.

      For a detailed read, head to the NLP article on D-SEAL

      2. Intuitive Model Selection 

      Our platform features a user-friendly interface for knowledge distillation. You can easily select the Teacher Model available on the DataNeuron platform, such as a suitable high-performing model like Llama 2 70 B.

      For the Student Model, you have flexible parameter options to tailor the distilled output to your deployment requirements. Choose from the DataNeuron provided options such as Llama 2 1B, Llama 2 3B, or Llama 2 13B parameters, balancing model size, computational cost, and performance. These options allow you to optimize for various deployment environments.

      3. Distillation Engine

      The heart of LLM Studio is our powerful distillation engine, which transfers knowledge from the selected teacher model to the smaller student model. The platform handles the underlying complications, allowing you to focus on your desired outcome.

      4. Inference & Deployment 

      Once the distillation process is complete, LLM Studio allows for rapid lean model testing, evaluation, and deployment. You can easily export them for on-device use, integrate them using an API, or deploy them within your cloud infrastructure.

      DataNeuron: Beyond Just Smaller Model

      At DataNeuron, distillation does more than just shrinking the model size; we create smarter, cost-efficient, and universally deployable AI solutions. 

      Real-World Impact: Distillation In Action

      Internal Search & RAG on a Budget

      Such distilled models can still be used to power an internal search capable of domain-specific answering, effectively implemented on a modest cloud setting.

      Why Distillation Is The Future of Scalable AI

      As foundation models grow in size, competence, and cost, businesses must address the main challenge of scaling their AI applications economically. Model distillation provides an attractive and accessible way ahead.

      With DataNeuron LLM Studio, that path is no longer just for field experts and infrastructure engineers. Whether you’re working on mobile apps, internal tools, or public NLP-facing products, training, distilling, and deploying LLMs is simple when you’re associated with us. Smarter models. Smaller footprints. All made easy by DataNeuron.

      Ready to see it in action? Book a demo or go through our product walkthrough.