How LLMs Generate Data-Rich Predictions

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 Instead of relying solely on what an AI model learned during training (which can quickly become outdated), RAG enables the model to retrieve relevant data from external databases, documents, or APIs—and then use that information to generate more accurate,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 Improves accuracy: By referencing up-to-date or proprietary data, RAG reduces outdated or incorrect answers.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

If you’re an AI engineer trying to understand how reasoning actually works inside LLMs, this will help you connect the dots. Most large language models can generate. But reasoning models can decide. Traditional LLMs followed a straight line: Input → Predict → Output. No self-checking, no branching, no exploration. Reasoning models introduced structure, a way for models to explore multiple paths, score their own reasoning, and refine their answers. We started with Chain-of-Thought (CoT) reasoning, then extended to Tree-of-Thought (ToT) for branching, and now to Graph-based reasoning, where models connect, merge, or revisit partial thoughts before concluding. This evolution changes how LLMs solve problems. Instead of guessing the next token, they learn to search the reasoning space- exploring alternatives, evaluating confidence, and adapting dynamically. Different reasoning topologies serve different goals: • Chains for simple sequential reasoning • Trees for exploring multiple hypotheses • Graphs for revising and merging partial solutions Modern architectures (like OpenAI’s o-series reasoning models, Anthropic’s Claude reasoning stack, DeepSeek R series and DeepMind’s AlphaReasoning experiments) use this idea under the hood. They don’t just generate answers, they navigate reasoning trajectories, using adaptive depth-first or breadth-first exploration, depending on task uncertainty. Why this matters? • It reduces hallucinations by verifying intermediate steps • It improves interpretability since we can visualize reasoning paths • It boosts reliability for complex tasks like planning, coding, or tool orchestration The next phase of LLM development won’t be about more parameters, it’ll be about better reasoning architectures: topologies that can branch, score, and self-correct. I’ll be doing a deep dive on reasoning models soon on my Substack- exploring architectures, training approaches, and practical applications for engineers. If you haven’t subscribed yet, make sure you do: https://lnkd.in/dpBNr6Jg ♻️ Share this with your network 🔔 Follow along for more data science & AI insights

Excited to share a groundbreaking advancement in AI: KG-IRAG! I've been diving into this fascinating paper on Knowledge Graph-Based Iterative Retrieval-Augmented Generation (KG-IRAG), a novel framework that significantly enhances how Large Language Models (LLMs) handle complex temporal reasoning tasks. >> What makes KG-IRAG special? Unlike traditional RAG methods that often struggle with multi-step reasoning, KG-IRAG introduces an iterative approach that progressively gathers relevant data from knowledge graphs. This is particularly powerful for queries involving temporal dependencies - like planning trips based on weather conditions or traffic patterns. The framework employs two collaborative LLMs: - LLM1 identifies the initial exploration plan and generates a reasoning prompt - LLM2 evaluates retrieved data and determines if further retrieval steps are needed The magic happens in the iterative retrieval process, where the system incrementally explores the knowledge graph, retrieving only what's needed when it's needed. This prevents information overload while ensuring comprehensive data collection. >> Technical implementation details: The researchers constructed knowledge graphs treating time, location, and event status as key entities, with relationships capturing temporal, spatial, and event-based correlations. Their approach models time as an entity for easier retrieval and reasoning. The system follows a sophisticated algorithm where: 1. Initial time/location parameters are identified 2. Relevant triplets are retrieved from the KG 3. LLM2 evaluates if current data is sufficient 4. If insufficient, search criteria are adjusted based on detected "abnormal events" 5. This continues until enough information is gathered to generate an accurate answer >> Impressive results: The researchers at UNSW, MBZUAI and TII evaluated KG-IRAG on three custom datasets (weatherQA-Irish, weatherQA-Sydney, and trafficQA-TFNSW), demonstrating significant improvements in accuracy for complex temporal reasoning tasks compared to standard RAG methods. What's particularly impressive is how KG-IRAG outperforms other approaches on questions requiring dynamic temporal reasoning - like determining the latest time to leave early or earliest time to leave late to avoid adverse conditions. This work represents a significant step forward in making LLMs more capable of handling real-world temporal reasoning tasks. Excited to see how this technology evolves!

If you want an example of how AI empowers data scientists, consider this one: A new study shows how we can use LLMs to harness unstructured data and the knowledge embedded in their pre-training to drive significant reduction in variance in experiments (think CUPED, but for multimodal data). 𝐋𝐋𝐌𝐬 𝐥𝐚𝐜𝐤 𝐠𝐮𝐚𝐫𝐚𝐧𝐭𝐞𝐞𝐬 𝐨𝐧 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲, and naively using them to predict counterfactuals isn't statistically valid. Few-shot LLMs rely on a small set of demo examples. This makes predictions variable and correlated across observations, breaking the independence assumption for valid causal analysis. The study shows 𝐡𝐨𝐰 𝐭𝐨 𝐮𝐬𝐞 𝐋𝐋𝐌𝐬 𝐭𝐨 𝐫𝐞𝐝𝐮𝐜𝐞 𝐯𝐚𝐫𝐢𝐚𝐧𝐜𝐞 in a principled way:  • Calibration - give more weight to subpopulations where the LLM predictions align with observed outcomes.    • Resampling-based aggregation - average across many random demo sets to neutralize variability.  • Three-way sample splitting - separate data used for examples, prediction, and estimation to preserve independence. The framework is conceptually similar to double machine learning (2ML) - blending causal inference and machine learning. So how do we get this kind of unstructured data? Surveys are one way to go. Ping us at Causara if you'd like to chat about marketing experimentation.

🚀 Why Generation is Better for Prediction? 📖🤖 Excited to share our latest paper: "Predicting Through Generation: Why Generation Is Better for Prediction" 🎉 Traditional prediction methods rely on pooling representations before making decisions. But does this really make the best use of the model’s knowledge? Our paper shows that token-level generation retains more information, making it a superior method for structured prediction tasks. 🔍 Why? Using Data Processing Inequality (DPI), we theoretically prove that generating tokens step by step preserves more mutual information than pooled representations. Simply put, when we force a model to summarize everything into a single vector, it loses critical details—but when it generates predictions token-by-token, it retains richer task-relevant knowledge. 🤯 The Challenge While generation offers more information, it introduces exposure bias (training vs. inference mismatch) and format mismatch (structured outputs vs. discrete tokens). ✨ Our Solution: PredGen We introduce PredGen, an end-to-end framework that: ✅ Uses scheduled sampling to reduce exposure bias ✅ Introduces a task adapter to transform generated tokens into structured outputs ✅ Aligns generation with prediction using Writer-Director Alignment Loss (WDAL) 📊 Results? PredGen consistently outperforms traditional classifiers and generative models across multiple classification and regression benchmarks. Our findings suggest that prediction through generation is the natural way forward for LLMs! Check out the full paper: 🔗https://lnkd.in/e5e4D72B Let’s discuss! Do you think generation-based approaches will shape the future of structured predictions? Grateful to my amazing co-authors and Advisors for making this this possible! Nusrat Prottasha, Prakash Bhat, Dr. Chun-Nam Yu, Dr. Mojtaba Soltanalian, Dr. Ivan Garibay, Ph.D. Dr. Ozlem Ozmen Garibay, Dr. Chen Chen, Dr. Niloofar Yousefi, Ph.D. 🚀 #AI #MachineLearning #LLMs #GenerativeAI #NLPResearch