Ensuring LLM Accuracy in Subjective Question Responses

RAG stands for Retrieval-Augmented Generation. It’s a technique that combines the power of LLMs with real-time access to external information sources. Instead of relying solely on what an AI model learned during training (which can quickly become outdated), RAG enables the model to retrieve relevant data from external databases, documents, or APIs—and then use that information to generate more accurate, context-aware responses. How does RAG work? 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗲: The system searches for the most relevant documents or data based on your query, using advanced search methods like semantic or vector search. 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Instead of just using the original question, RAG 𝗮𝘂𝗴𝗺𝗲𝗻𝘁𝘀 (enriches) the prompt by adding the retrieved information directly into the input for the AI model. This means the model doesn’t just rely on what it “remembers” from training—it now sees your question 𝘱𝘭𝘶𝘴 the latest, domain-specific context 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲: The LLM takes the retrieved information and crafts a well-informed, natural language response. 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗥𝗔𝗚 𝗺𝗮𝘁𝘁𝗲𝗿? Improves accuracy: By referencing up-to-date or proprietary data, RAG reduces outdated or incorrect answers. Context-aware: Responses are tailored using the latest information, not just what the model “remembers.” Reduces hallucinations: RAG helps prevent AI from making up facts by grounding answers in real sources. Example: Imagine asking an AI assistant, “What are the latest trends in renewable energy?” A traditional LLM might give you a general answer based on old data. With RAG, the model first searches for the most recent articles and reports, then synthesizes a response grounded in that up-to-date information. Illustration by Deepak Bhardwaj

Explaining the Evaluation method LLM-as-a-Judge (LLMaaJ). Token-based metrics like BLEU or ROUGE are still useful for structured tasks like translation or summarization. But for open-ended answers, RAG copilots, or complex enterprise prompts, they often miss the bigger picture. That’s where LLMaaJ changes the game. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝘁? You use a powerful LLM as an evaluator, not a generator. It’s given: - The original question - The generated answer - And the retrieved context or gold answer 𝗧𝗵𝗲𝗻 𝗶𝘁 𝗮𝘀𝘀𝗲𝘀𝘀𝗲𝘀: ✅ Faithfulness to the source ✅ Factual accuracy ✅ Semantic alignment—even if phrased differently 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: LLMaaJ captures what traditional metrics can’t. It understands paraphrasing. It flags hallucinations. It mirrors human judgment, which is critical when deploying GenAI systems in the enterprise. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗟𝗟𝗠𝗮𝗮𝗝-𝗯𝗮𝘀𝗲𝗱 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: - Answer correctness - Answer faithfulness - Coherence, tone, and even reasoning quality 📌 If you’re building enterprise-grade copilots or RAG workflows, LLMaaJ is how you scale QA beyond manual reviews. To put LLMaaJ into practice, check out EvalAssist; a new tool from IBM Research. It offers a web-based UI to streamline LLM evaluations: - Refine your criteria iteratively using Unitxt - Generate structured evaluations - Export as Jupyter notebooks to scale effortlessly A powerful way to bring LLM-as-a-Judge into your QA stack. - Get Started guide: https://lnkd.in/g4QP3-Ue - Demo Site: https://lnkd.in/gUSrV65s - Github Repo: https://lnkd.in/gPVEQRtv - Whitepapers: https://lnkd.in/gnHi6SeW

Small variations in prompts can lead to very different LLM responses. Research that measures LLM prompt sensitivity uncovers what matters, and the strategies to get the best outcomes. A new framework for prompt sensitivity, ProSA, shows that response robustness increases with factors including higher model confidence, few-shot examples, and larger model size. Some strategies you should consider given these findings: 💡 Understand Prompt Sensitivity and Test Variability: LLMs can produce different responses with minor rephrasings of the same prompt. Testing multiple prompt versions is essential, as even small wording adjustments can significantly impact the outcome. Organizations may benefit from creating a library of proven prompts, noting which styles perform best for different types of queries. 🧩 Integrate Few-Shot Examples for Consistency: Including few-shot examples (demonstrative samples within prompts) enhances the stability of responses, especially in larger models. For complex or high-priority tasks, adding a few-shot structure can reduce prompt sensitivity. Standardizing few-shot examples in key prompts across the organization helps ensure consistent output. 🧠 Match Prompt Style to Task Complexity: Different tasks benefit from different prompt strategies. Knowledge-based tasks like basic Q&A are generally less sensitive to prompt variations than complex, reasoning-heavy tasks, such as coding or creative requests. For these complex tasks, using structured, example-rich prompts can improve response reliability. 📈 Use Decoding Confidence as a Quality Check: High decoding confidence—the model’s level of certainty in its responses—indicates robustness against prompt variations. Organizations can track confidence scores to flag low-confidence responses and identify prompts that might need adjustment, enhancing the overall quality of outputs. 📜 Standardize Prompt Templates for Reliability: Simple, standardized templates reduce prompt sensitivity across users and tasks. For frequent or critical applications, well-designed, straightforward prompt templates minimize variability in responses. Organizations should consider a “best-practices” prompt set that can be shared across teams to ensure reliable outcomes. 🔄 Regularly Review and Optimize Prompts: As LLMs evolve, so may prompt performance. Routine prompt evaluations help organizations adapt to model changes and maintain high-quality, reliable responses over time. Regularly revisiting and refining key prompts ensures they stay aligned with the latest LLM behavior. Link to paper in comments.

We've been lied to about RAG. It doesn't fix hallucinations. It doesn't guarantee accurate answers. And in production? It often fails quietly—giving plausible but incomplete responses that erode trust. I believed in basic RAG like everyone else. Split docs. Embed chunks. Retrieve. Prompt. Done. But when we deployed it, users were frustrated. Answers missed key details. Relevant info was in the database but not in the response. Sometimes the model just… made things up anyway. So, what is the solution? RAG isn't a solution—it's a starting point. Want to learn what actually works in real-world systems? RAG Plus. Not a single tool. A layered evolution that turns broken prototypes into trusted AI. Here's what separates basic RAG from production-grade performance: ✅ Hybrid Search – Combine vector + keyword search. One misses synonyms, the other misses semantics. Use both. ✅ Query Expansion – Let an LLM rephrase the user's question 3–5 ways. Suddenly, "reset password" also finds "account recovery." ✅ Agentic Retrieval – Stop retrieving once. Let the AI decide what it needs, then go get it—iteratively. ✅ Re-ranking – Retrieve 20, use a cross-encoder to rank, return top 5. Precision skyrockets. ✅ Feedback Loops – Log ratings, comments, and retrieval paths. Your system should learn from every failure. Result? → 40–60% gains in answer accuracy → 73% fewer follow-up searches by experts → 4x adoption in internal knowledge tools Are you already using RAG.? But are you stuck at "good enough"? 👉 Are you still relying on pure vector search? 👉 Do your chunks lack document structure or context? 👉 Is your LLM guessing—or reasoning? If yes, it's time for RAG Plus. What's one bottleneck killing your RAG performance today? Want to deep diver into a framework for RAG+? Navigate to the link in the comments. #AI #MachineLearning #RAG #LLM #ArtificialIntelligence #AzureAIFoundry #dougortiz

LLM pro tip to reduce hallucinations and improve performance: instruct the language model to ask clarifying questions in your prompt. Add a directive like "If any part of the question/task is unclear or lacks sufficient context, ask clarifying questions before providing an answer" to your system prompt. This will: (1) Reduce ambiguity - forcing the model to acknowledge knowledge gaps rather than filling them with hallucinations (2) Improve accuracy - enabling the model to gather necessary details before committing to an answer (3) Enhance interaction - creating a more natural, iterative conversation flow similar to human exchanges This approach was validated in the 2023 CALM paper, which showed that selectively asking clarifying questions for ambiguous inputs increased question-answering accuracy without negatively affecting responses to unambiguous queries https://lnkd.in/gnAhZ5zM

One of the major hurdles in adopting LLMs for many companies has been the risk of model-generated hallucinations and a lack of transparency. In response to this, there has been a concerted effort to enhance monitoring mechanisms and establish robust checks and balances around these models. This includes the development of more transparent evaluation models that align closely with human judgment (JudgeLM - https://lnkd.in/e8Xspek9), the implementation of customizable evaluation criteria tailored to specific business needs (FoFo - https://lnkd.in/eAMutjXJ), and open-source frameworks like DeepEval (https://lnkd.in/eYMB-Xiw ) which track aspects such as toxicity and hallucination using a variety of NLP models such as QA bi-encoders, vector similarity tools, and NLI models. This week, a few additional methods and frameworks were intoruduced: 1. Cleanlab's 𝗧𝗿𝘂𝘀𝘁𝘄𝗼𝗿𝘁𝗵𝘆 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 (𝗧𝗟𝗠) incorporates a trust score for each output, enhancing reliability and transparency by indicating the likelihood of an output being accurate. This framework is particularly helpful for customer facing applications and where the cost of errors is high (https://lnkd.in/e9FUztgj) 2. 𝗣𝗿𝗼𝗺𝗲𝘁𝗵𝗲𝘂𝘀 𝟮 (https://lnkd.in/er_3nqGt) released by LG researchers is developed using weight merging of separately trained evaluators: one that directly scores the outputs (direct assessment) and another that ranks the outputs (pairwise ranking). In extensive benchmark tests across both direct assessment and pairwise ranking, Prometheus 2 achieved the highest correlations and agreement scores with human evaluators, demonstrating a substantial advancement over existing methods. 3. “When to Retrieve” (https://lnkd.in/euANnwWg) presents an innovative model, the 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗟𝗟𝗠 (𝗔𝗗𝗔𝗣𝗧-𝗟𝗟𝗠), which intelligently decides when to utilize external information retrieval to enhance its question-answering capabilities. ADAPT-LLM is trained to generate a special token ⟨RET⟩ when it needs more information to answer a question, signifying a retrieval is necessary. Conversely, it relies on its intrinsic knowledge when confident in its response. This model outperformed fixed strategies like always retrieving information or solely relying on its own memory. There is no doubt that significant investments are being made to enhance the robustness and reliability of LLMs, with stringent checks and balances being established. Meanwhile, tech giants like Google are not shying away from deploying LLMs in sensitive areas, as seen with their MedGemini project - a highly capable multimodal model specialized in medicine, demonstrating superior performance in medical benchmarks and tasks (https://lnkd.in/epbv63iN). These developments indicate a rapid progression towards broader and more impactful deployments of LLMs in critical sectors soon.

Tackling Hallucination in LLMs: Mitigation & Evaluation Strategies As Large Language Models (LLMs) redefine how we interact with AI, one critical challenge is hallucination—when models generate false or misleading responses. This issue affects the reliability of LLMs, particularly in high-stakes applications like healthcare, legal, and education. To ensure trustworthiness, it’s essential to adopt robust strategies for mitigating and evaluating hallucination. The workflow outlined above presents a structured approach to addressing this challenge: 1️⃣ Hallucination QA Set Generation Starting with a raw corpus, we process knowledge bases and apply weighted sampling to create diverse, high-quality datasets. This includes generating baseline questions, multi-context queries, and complex reasoning tasks, ensuring a comprehensive evaluation framework. Rigorous filtering and quality checks ensure datasets are robust and aligned with real-world complexities. 2️⃣ Hallucination Benchmarking By pre-processing datasets, answers are categorized as correct or hallucinated, providing a benchmark for model performance. This phase involves tools like classification models and text generation to assess reliability under various conditions. 3️⃣ Hallucination Mitigation Strategies In-Context Learning: Enhancing output reliability by incorporating examples directly in the prompt. Retrieval-Augmented Generation: Supplementing model responses with real-time data retrieval. Parameter-Efficient Fine-Tuning: Fine-tuning targeted parts of the model for specific tasks. By implementing these strategies, we can significantly reduce hallucination risks, ensuring LLMs deliver accurate and context-aware responses across diverse applications. 💡 What strategies do you employ to minimize hallucination in AI systems? Let’s discuss and learn together in the comments!

Here is a step-by-step guide for successfully finetuning your own LLM judge on granular / domain-specific evaluation tasks… Background: LLM-as-a-Judge is a reference-free evaluation technique that prompts an off-the-shelf / proprietary LLM to evaluate the output of another LLM. This approach is effective, but it has limitations: - LLM APIs are not transparent and come with security concerns. - Updates to the model (which we can’t control) impact evaluation results. - Every call to the LLM judge costs money, so cost can become a concern. Proprietary LLMs are also best at tasks that are aligned with their training data, tend to avoid providing strong scores / opinions, and may struggle with domain-specific evaluation. For these reasons, we may want to finetune our own LLM judge using the steps below. (1) Solidify the evaluation criteria: The first step of evaluation is deciding what exactly we want to evaluate. We should: - Outline a specific set of criteria that we care about. - Write a detailed description for each of these criteria. Over time, we must evolve, refine, and expand our criteria as we better understand our evaluation task. (2) Prepare a dataset: Human-labeled data allows us to finetune and evaluate our LLM judge. Finetuning an LLM judge will require ~1K-100K evaluation examples, and collecting more / better data is always beneficial. Each example should contain an input instruction, a response, a description of the evaluation criteria, and a scoring rubric. Each input is paired with a scoring rationale and a final result (e.g., a 1-5 Likert score). (2.5) Use synthetic data: Using purely synthetic training data can introduce bias by exposing the model to a narrow distribution of data, but combining human / synthetic data can be effective. For example, check out Constitutional AI [1] or RLAIF [2]. (3) Focus on the rationales: We obviously want the scores over which the LLM judge is trained to be accurate, but we should also create high-quality rationales for each score. Tweaking the rationales over which the LLM judge is trained can make the model more helpful. (4) Use reference answers: This step is optional, but we can prepare reference answers for each example in the dataset. Reference answers simplify evaluation by allowing the LLM judge to compare the response to a reference instead of having to score the response in an absolute manner. (5) Train the model: Once all of our data (and optionally reference answers) have been collected, then we can train our LLM judge using a basic SFT approach. Finetuning an LLM judge is technically no different than any other instruction tuning task! For a full implementation of this process, check out the Prometheus papers [3, 4, 5]. This work shows that we can create highly-accurate, domain-specific evaluation models–even surpassing the performance of LLM-as-a-Judge with proprietary LLMs– by simply finetuning an LLM on a small amount of data that is relevant to our evaluation task.

Throw out the old #RAG approaches; use Corrective RAG instead! Corrective RAG introduces the additional layer of checking and correcting retrieved documents, ensuring more accurate and relevant information before generating a final response. This approach enhances the reliability of the generated answers by refining or correcting the retrieved context dynamically. The key idea here is to retrieve document chunks from the vector database as usual and then use an LLM to check if each retrieved document chunk is relevant to the input question. The process roughly goes as below, ⮕ Step 1: Retrieve context documents from vector database from the input query. ⮕ Step 2: Use an LLM to check if retrieved documents are relevant to the input question. ⮕ Step 3: If all documents are relevant (Correct), no specific action is needed. ⮕ Step 4: If some or all documents are not relevant (Ambiguous or Incorrect), rephrase the query and search the web to get relevant context information. ⮕ Step 5: Send rephrased query and context documents or information to the LLM for response generation. I have made a complete video on corrective RAG using LangGraph: https://lnkd.in/gKaEjEvk Know more in-depth about corrective RAG in this paper: https://lnkd.in/g8FkrMzS

Herels How To Use HyDE When RAG Fails: A Practical Workflow --- I have been following the advice of advice of Avi Chawla and Daily Dose of Data Science. They offered the great graphic below. RAG works great for me. Right up until someone asks it a vague question. In customer support, internal search, or training, users rarely use the right technical terms. They say “the screen freezes” instead of “memory leak in main thread.” Standard RAG misses these queries because the "vector distance" (sorry for the suddent Geek Speak) is too wide. HyDE (Hypothetical Document Embedding) fixes this by searching with a hypothetical answer instead of the raw question. Here is the step-by-step workflow: STEP 1. Generate a Hypothetical Answer Pass the user’s question to an LLM first. Prompt it to write a short, plausible answer using proper vocabulary. The answer can be wrong—you just need realistic language and structure. Example prompt: “Write a short paragraph that answers this question as an expert would, using correct technical terms.” STEP 2. Embed the Hypothetical Answer Run the generated text through your embedding model. This creates a vector that sits much closer to your technical documentation than the original vague question. Now I get it, before you mention it. Yes, there might be hallucinations here. STEP 3. Retrieve with the Hypothetical Vector (yep, Geek Speak returns briefly) Search your vector database using the hypothetical answer’s embedding. This shifts retrieval from Question-to-Answer to Answer-to-Answer, which dramatically boosts similarity scores. STEP 4. Generate the Final Response Feed the retrieved documents back into the LLM with a grounded prompt: “Use only the context below to answer the original question accurately and clearly.” Where This Works For Me (and probably will for you) Customer Support: Turn “It’s slow” into “Potential database indexing issue” and find the right KB article. Enterprise Search: Map “quarterly sales slide” to the actual “Q3 Revenue Dashboard Template.” Technical Documentation: Connect “how to connect my app” to “OAuth2 implementation guide.” AI Training: Bridge novice symptom-language to expert root-cause language instantly. HyDE makes search feel less like a keyword matcher and more like a senior teammate who translates intent into execution.