Using Contextual Data in Domain-Specific AI Models

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 Instead of relying solely on what an AI model learned during training (which can quickly become outdated), RAG enables the model to retrieve relevant data from external databases, documents, or APIs—and then use that information to generate more accurate,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 Improves accuracy: By referencing up-to-date or proprietary data, RAG reduces outdated or incorrect answers.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

Context-aware agents require deliberate architecture that combines retrieval-augmented generation, session memory, and adaptive reasoning. This 10-step framework begins with defining the agent’s domain, use cases, and output structure, followed by ingestion and chunking of trustworthy data aligned to safety and alignment principles. Embeddings are then generated using models like OpenAI or Cohere and stored in vector databases such as FAISS or Pinecone for efficient semantic retrieval. Retrieval logic leverages k-NN search to fetch relevant chunks based on similarity and metadata filters. Prompts are engineered dynamically using retrieved context, optionally enriched with few-shot examples, and sent to LLMs like GPT-4 or Claude with configurable parameters. Session memory can be integrated to track interaction history and enhance continuity. Continuous evaluation identifies hallucinations, prompt failures, and edge cases for iterative refinement. Deployment involves wrapping the agent in an API or interface with monitoring hooks, and expansion includes tool use, personalization, and self-corrective mechanisms. If you follow this framework, you’ll be building the pipeline forming the backbone of production-grade AI agents that reason with context and respond with precision. Go build! #genai #aiagent #artificialintelligence

Had to share the one prompt that has transformed how I approach AI research. 📌 Save this post. Don’t just ask for point-in-time data like a junior PM. Instead, build in more temporal context through systematic data collection over time. Use this prompt to become a superforecaster with the help of AI. Great for product ideation, competitive research, finance, investing, etc. ⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰ TIME MACHINE PROMPT: Execute longitudinal analysis on [TOPIC]. First, establish baseline parameters: define the standard refresh interval for this domain based on market dynamics (enterprise adoption cycles, regulatory changes, technology maturity curves). For example, AI refresh cycle may be two weeks, clothing may be 3 months, construction may be 2 years. Calculate n=3 data points spanning 2 full cycles. For each time period, collect: (1) quantitative metrics (adoption rates, market share, pricing models), (2) qualitative factors (user sentiment, competitive positioning, external catalysts), (3) ecosystem dependencies (infrastructure requirements, complementary products, capital climate, regulatory environment). Structure output as: Current State Analysis → T-1 Comparative Analysis → T-2 Historical Baseline → Delta Analysis with statistical significance → Trajectory Modeling with confidence intervals across each prediction. Include data sources. ⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰

There’s been a lot of talk about making LLM outputs more deterministic – especially surrounding agents. What’s often overlooked in the push for deterministic outputs is the input itself: context. In most enterprise AI systems, “context” is still treated as raw data. But to answer complex, multi-hop questions like “How is engineering project Y tracking against its OKRs?”, agents need a deeper understanding of cross-system relationships, enterprise-specific language, and how work actually gets done. LLMs aren’t built to infer this on their own. They need a machine-readable map of enterprise knowledge – something consumer search systems have long relied on: the knowledge graph. But applying that in the enterprise brings a new set of challenges: the graph must enforce data privacy, reason over small or fragmented datasets without manual review, and do so using scalable algorithms. At Glean, we’ve built a knowledge graph with thousands of edges, recently expanded into a personal graph that captures not just enterprise data, but how individuals work. This foundation sets the stage for personalized, context-aware agents that can anticipate needs, adapt to organizational norms, and guide employees toward their goals, far beyond the limits of chat session history. We break this down in more detail in our latest engineering blog on how knowledge graphs ground enterprise AI and why they’re foundational to the future of agentic reasoning. https://lnkd.in/g-rVJPri

Just because you can read the instruments doesn’t mean you can fly the plane. AI needs the same context as everyone else. AI can use instrument data to guess the relationships between the data points. However, the biggest risk (crashing) has the least data to learn from. That means AI could become good at flying but not as good at avoiding crashes. Why? Crashes are just a low-frequency type of landing when the data lacks context. Labels are the simplest type of context. They contextualize data with outcomes and help AI learn the difference between a good and bad landing. Knowledge graphs contain more context about the relationships between data points like attitude, thrust, and altitude. What do you do if the nose is up and the altitude isn’t rising? The knowledge graph holds domain expertise that models can quickly integrate to fast-track training, create transparency, and improve reliability. Gathering data with context is a different approach to data engineering. For businesses, context is metadata about whatever generates the data point. It could be a recruiter selecting or rejecting a resume. Just logging the select/reject lacks context. At a minimum, we would save the job opportunity and resume with it. More context could be provided by a reason for the rejection: not enough experience, unwilling to relocate, wrong degree, etc. With 0 context, the model can’t learn anything from a rejection. With just the resume and job description, it takes a lot of them to guess the reason correctly. With a reason for the rejection, the model has enough context to accelerate mapping reasons to the parts of the resume that could have triggered the action. Knowledge graphs give models domain expertise using context. That’s why they are so powerful. #AI #DataEngineering #DataQuality

How I Explain Retrieval Augmented Generation (RAG) to Business Managers (in 3 Minutes) Large language models have been a huge hit for personal and consumer use cases. But what happens when you bring them into your business or use them for enterprise purposes? Well, you encounter a few challenges. The most significant one is the lack of domain expertise. Remember, these large language models are trained on publicly available datasets. This means they might not possess the detailed knowledge specific to your domain or niche. Moreover, the training data won't include your Standard Operating Procedures (SOPs), records, intellectual property (IP), guidelines, or other relevant content. So, if you're considering using AI assistants "out of the box," they're going to lack much of that context, rendering them nearly useless for your specific business needs. However, there's a solution that's becoming quite popular and has proven to be robust: RAG, or Retrieval Augmented Generation. In this approach, we add an extra step before a prompt is sent to an AI assistant. This step involves searching through a corpus of your own data—be it documents, PDFs, or transactions—to find information relevant to the user's prompt. The information found is then added to the prompt that goes into the AI assistant, which subsequently returns the answer to the user. It turns out this is an incredibly effective way to add context for an AI assistant. Doing so also helps reduce hallucinations, which is another major concern. Hope you find this overview helpful. Have any questions or comments? Please drop them below. If you're a AI practitioner and believe I've overlooked something or wish to contribute to the discussion, feel free to share your insights. Many people will be watching this, and your input could greatly benefit others. #rag #llms #aiforbusiness