Contextualized Knowledge Application

𝗠𝗮𝗸𝗶𝗻𝗴 𝗔𝗜 𝘀𝗺𝗮𝗿𝘁𝗲𝗿, 𝗳𝗮𝘀𝘁𝗲𝗿, 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲, 𝗼𝗻𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗮𝘁 𝗮 𝘁𝗶𝗺𝗲 As artificial intelligence systems mature, prompt engineering alone is no longer sufficient. The next stage of advancement is context engineering, which focuses on carefully designing everything an AI model sees before it responds. By controlling the information, structure, and memory available to the model, we enable higher accuracy, deeper reasoning, and more predictable performance. In prompt engineering, you provide a single instruction and rely on the model’s internal capabilities. In context engineering, you orchestrate multiple sources of information, selectively retrieve relevant knowledge, and structure the context so the model performs with greater precision. The result is a system that produces smarter, faster, and more consistent outcomes for complex tasks. 𝗞𝗲𝘆 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗼𝗳 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗮𝗻𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 AI systems perform best when provided with the most relevant information at the right time. Retrieval augmented generation techniques allow dynamic integration of documents, structured data, APIs, and real-time facts. By assembling only what is required, we reduce noise and improve reliability. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 Context engineering enables long sequence reasoning by handling thousands of tokens efficiently. It also supports structured integration where models combine tables, knowledge graphs, and stored facts with unstructured inputs. This approach allows the AI to reason more effectively and deliver responses grounded in verified information. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Advanced systems must balance short term and long-term memory. Context compression techniques maintain meaning while reducing size, enabling efficient responses without losing depth. Constraint management ensures token limits are respected while optimizing the information provided to the model. 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗮𝗻𝗱 𝗧𝗼𝗼𝗹𝘀 Developers can leverage tools and frameworks that enable dynamic context assembly and scalable retrieval, including LangChain, LlamaIndex, Pinecone, and Weaviate. Vector databases manage embeddings and memory hierarchies. Hybrid retrieval strategies combine structured APIs with multi document grounding. Testing and refinement cycles measure accuracy, optimize relevance, and improve system intelligence over time. 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Context engineering transforms AI from reactive to adaptive intelligence. Instead of issuing instructions and hoping for accuracy, we design the environment in which the model operates so that correct responses become the natural outcome. This approach powers enterprise-ready AI systems that are more predictable, more reliable, and capable of scaling across complex domains. Follow Umair Ahmad for more insights #AI #ContextEngineering #SystemDesign #MachineLearning

I just came across an insightful paper from researchers at Technical University of Munich that explores the critical components of Retrieval-Augmented Generation (RAG) systems. As RAG continues to gain traction in industry applications, this study provides valuable guidance on optimizing these systems. The researchers systematically evaluated three key aspects of RAG systems: 1. Context Size Impact: They discovered that performance steadily improves as you increase context snippets up to about 10-15 snippets, but then stagnates or even declines with 20-30 snippets. This "context saturation" point is crucial for balancing comprehensive information with cognitive overload. 2. Model Selection Matters: Different models excel in different domains. For biomedical question answering, Mixtral and Qwen outperformed larger models like GPT-4o and LLaMa 3 (70B). Meanwhile, for encyclopedic content, GPT and LLaMa performed better. This demonstrates that model size isn't always the determining factor in RAG performance. 3. Retrieval Method Comparison: When testing BM25 (sparse retrieval) against semantic search (dense retrieval) in open-domain settings, BM25 showed slightly better performance. This suggests that keyword-based precision can sometimes be more valuable than semantic similarity, especially in specialized domains. The study used two challenging datasets - BioASQ (biomedical) and QuoteSum (encyclopedic) - focusing on long-form question answering where comprehensive context utilization is essential. This moves beyond the typical factoid QA evaluation to more complex scenarios. The researchers also identified an interesting phenomenon where LLMs sometimes produced better answers using their internal knowledge than when provided with imperfect retrieved snippets - highlighting the ongoing challenge of knowledge conflicts in RAG systems. For anyone building RAG applications, this paper provides concrete guidance on optimizing context size, selecting appropriate models, and choosing retrieval methods based on your specific domain and requirements.

Banks are drowning in #data, starving for context. Travel today is a perfect mirror of #banking. You open one app for maps, another for your calendar, another for tickets, and maybe even a weather app. Tons of data, zero connection. You’re left stitching everything together yourself. Now imagine you have a meeting at 10am. Your phone knows the address from your calendar. It knows where you are right now. It knows how long it will take to get there. Instead of juggling apps, you get one #contextual notification: “Leave in 5 minutes. Take the MRT feeder and you’ll arrive on time.” That’s not data. That’s action. Banks are stuck in the same trap. They collect endless streams of #transactions, demographics, preferences… and then throw generic #dashboards and irrelevant offers at customers. No context. No action. Just noise. #Context is what transforms a dataset into a useful journey. It’s the difference between a bank app that shows me a balance and one that tells me I’m about to miss a bill, helps me build an emergency fund, or warns me I won’t reach my #savings goal unless I adjust now how much I'm spending. Data without context is clutter. Context without action is pointless.

What is Retrieval-Augmented Generation? RAG is a dual-pronged methodology that enhances language models by merging information retrieval with text generation. It leverages a pre-existing knowledge base—sourced from encyclopedias, databases, and more—to augment the content generation process. This fusion addresses concerns such as "AI hallucinations" and ensures data freshness, creating more accurate and contextually aware outputs. Practical Applications of RAG RAG shines in knowledge-intensive NLP tasks by integrating retrieval and generation mechanisms. This approach is particularly beneficial in domains requiring a deep understanding of complex information. For instance, a customer inquiring about the latest software features will receive the most recent and relevant information, fetched from dynamic sources like release notes or official documentation. Active Retrieval-Augmented Generation Active Retrieval-Augmented Generation goes a step further by actively retrieving and integrating up-to-date information during interactions. This enhances the model’s responsiveness in dynamic environments, making it ideal for applications that demand real-time accuracy. For example, in news summarization, RAG can provide timely and accurate updates by incorporating the latest developments. RAG vs. Fine-Tuning RAG's strength lies in blending pre-existing knowledge with creative generation, offering a nuanced and balanced approach. While fine-tuning focuses on refining a model’s performance on specific tasks, RAG’s combination of retrieval and generation proves advantageous for knowledge-intensive tasks, providing a sophisticated understanding of context. The Future of RAG Retrieval-Augmented Language Models (RALLM) encapsulate the essence of retrieval augmentation, seamlessly integrating contextual information retrieval with the generation process. RAG is not just a technological advancement; it represents a paradigm shift in how we approach AI and language models. Prominent Use Cases of RAG Customer Support: Companies like IBM use RAG to enhance customer-care chatbots, ensuring interactions are grounded in reliable and up-to-date information, providing personalized and accurate responses. Healthcare: RAG can assist medical professionals by retrieving the latest research and medical guidelines to support clinical decision-making and patient care. Legal Research: Lawyers can leverage RAG to quickly access and synthesize relevant case laws, statutes, and legal precedents, enhancing their ability to prepare cases and provide legal advice. Academic Research: Researchers can use RAG to gather and integrate the latest studies and data, streamlining literature reviews and enhancing the quality of academic papers.

✨ Contextual Intelligence: The Next Frontier for AI Agents? ⬇️ I've been diving into some research on AI-driven personalization and came across a trend that many businesses are overlooking. The next frontier in creating standout experiences is all about contextual intelligence, understanding a client's real-time surroundings, history, and needs to deliver interactions that feel genuinely tailored to them. Personalization is no longer a luxury; it's an expectation. According to McKinsey, 76% of consumers feel disappointed when companies don't offer personalized experiences. Salesforce research shows that 62% of consumers might switch brands if they don't get it. But here's the catch: traditional personalization, like addressing someone by name or referencing their purchase history, just doesn't cut it anymore. Going Beyond the Basics: Why Context Matters Classic personalization leans heavily on historical data, like what users have browsed or bought. While useful, this only tells you who the customer was, not what they need now. Contextual intelligence changes the game by adding real-time factors: • Time of day • User's location • Persona • Targeted Voice • Current activity • Memory of previous interactions... By incorporating these insights, AI can make real-time adjustments that feel immediate, relevant, and personal. Why Context is Critical for Growth Adopting contextual intelligence isn't just about keeping up, it's about staying ahead. While many companies are still stuck in basic personalization, forward-thinking firms are creating experiences that make customers feel truly seen. There's also a huge privacy advantage. Contextual intelligence doesn't rely on invasive personal data. Instead, it uses anonymous signals like time, location, or device type, allowing businesses to stay compliant with privacy laws while still delivering relevance. And let's not forget the "wow" factor. When apps or websites effortlessly adapt to a user's immediate needs, it creates a sense of delight that builds deep loyalty. The future belongs to bespoke experiences that go beyond preferences and master the art of context.

If knowledge was power, AI has made context currency Generative AI turned public knowledge into a commodity. The edge now is context: the scarce, proprietary fabric of how you work; your principles, processes, language, workflows, and the tacit judgment living in veteran minds. That’s the stuff competitors can’t scrape. Think of context like a connect‑the‑dots puzzle: provide the right dots, in the right order, at the right resolution, and AI snaps into focus. Too few dots and you get generic guesses. Too many unstructured dots and you get slower, noisier, pricier output. Contextual noise with financial, operational, and strategic costs. The real unlock is shifting from “artificial intelligence” to Architected Intelligence. The models are table stakes; advantage comes from how you architect your proprietary context into them so outputs are consistent, governed, and traceable by design. Leaders need to treat context stewardship as a priority. Retain the humans who hold institutional memory, and reward knowledge‑sharing. As generic knowledge gets cheaper, your differentiator is the judgment you can scale. I unpack a beginning playbook, including governance and architecture patterns, in the article below.

Context: The Missing Piece in Your AI Strategy I've been vocal about data quality being crucial when creating AI applications. While quality remains foundational, there's another critical element that determines success: CONTEXT. Context isn't just about having information your LLM can access—it's about having the RIGHT information with the PROPER framing. It's knowing which 10 pages actually matter when faced with 10,000 pages of documentation. Why Context Matters 🔍 Without proper context, even the most advanced AI models struggle: • ❌ Hallucinations: Models generate plausible but false information • ❌ Unreliable outputs: Responses lack relevance to specific business needs • ❌ Lost credibility: Users quickly lose trust in AI tools that miss the mark As LLM capabilities increase, the success of AI products will depend much more on context quality than raw model performance. The models are already quite capable when context is shaped properly! Overcoming the Context Challenge 💪 Here's how forward-thinking teams are tackling this: 1️⃣ Invest in internal data management early • Data annotation and observability become critical • Know when you need more data, when it's outdated, and when experts must be involved 2️⃣ Build rigorous evaluation frameworks • Measure context relevance and sufficiency before deployment • Create feedback loops to continuously improve context quality 3️⃣ Experiment and iterate deliberately • Recognize the tight feedback loop between context and inference • Good context leads to better inference, which leads to better context This is why at DataMantis, our team works closely with Subject Matter Experts to understand and gather as much contextual information as possible. We meticulously craft prompts and build RAG systems that provide rich context to our AI models, ensuring responses that closely match what an expert would provide. This approach has dramatically improved accuracy for the SMBs we serve, giving them enterprise-grade AI capabilities from day one. The next frontier in AI isn't just about bigger models—it's about smarter context. For businesses looking to truly differentiate their AI offerings, focusing on context quality might be your most strategic investment yet. What context challenges are you facing with your AI implementations? #DataMantis #MantisAI #ArtificialIntelligence #ContextualAI #DataQuality #EnterpriseAI #LLMs

Want to build AI applications that leverage your organization's knowledge base? Here's how Retrieval-Augmented Generation (RAG) is changing the game. **RAG Architecture Demystified: Building Smarter AI Applications** Ever asked a chatbot about your company's product and received outdated information? That's where RAG comes in. Instead of relying solely on an AI model's training data, RAG enables your applications to reference your specific documents, databases, and internal knowledge when generating responses. Key insights from my latest technical deep-dive: 🔹 Framework Selection: After extensive testing, LangChain emerged as the top choice for RAG implementations, offering robust retrieval pipelines and extensive integrations. Our comparison with LlamaIndex and Haystack revealed key tradeoffs in learning curve, customization, and production readiness. 🔹 Vector Storage Solutions: MongoDB Atlas Vector Search stood out among competitors (AstraDB, Weaviate) for its automatic scaling, familiar query syntax, and cost-effective operation at scale. 🔹 Architecture Breakdown: I've included a detailed architectural diagram showing how document processing, embedding pipelines, query processing, and response generation work together in a production environment. The most exciting part? This isn't just theory. I've included practical implementation code and real-world optimization tips you can use today. Whether you're building customer support systems, research tools, or knowledge management solutions, RAG can transform how your AI applications interact with your organization's data. What challenges have you faced when implementing AI solutions in your organization? How are you handling context and knowledge retrieval? #ArtificialIntelligence #SoftwareEngineering #RAG #AIEngineering #LangChain

🚀 Combining Large Context Window Models with Graph RAG for State-of-the-Art AI Integrations 🌐 As AI systems evolve, handling complex datasets and interconnected knowledge requires innovative approaches. In my latest blog, I explore how large context window models like Gemini 1.5 Pro combined with Graph Retrieval-Augmented Generation (Graph RAG) and context caching can strike the right balance between performance, latency, cost, and accuracy. 🔍 Key Takeaways: Improved Latency: Large context windows reduce the need for repeated queries, speeding up response times. Reduced Token Costs: Cache the graph and pass it through the large context window to minimize token usage. Better Contextual Reasoning: Hold more relationships in memory for accurate multi-hop reasoning in complex domains. 💡 Best Practices: Use pre-constructed graphs for static knowledge sources. Leverage context caching for frequently accessed data to improve efficiency. In dynamic domains, employ a hybrid approach with real-time updates for relevant sections of the graph. 🎯 Real-World Applications: Customer Support Systems: Cache frequently asked questions for quicker resolutions and reduced costs. E-Commerce Recommendations: Combine static product catalogs with real-time updates based on user behavior. Scientific Research: Connect related findings for faster and more accurate reasoning. By combining Graph RAG, Text2Emb, and Gemini for large context windows, we can achieve more powerful and dynamic retrieval, reducing latency and costs while enhancing AI's reasoning capabilities. 🧠 The future of AI lies in fine-tuning these approaches for specific use cases—be it real-time analytics, healthcare, or legal research. Check out the full blog for a deeper dive into best practices, trade-offs, and real-world use cases. 👇 #AI #MachineLearning #GraphRAG #GenerativeAI #AIResearch #ContextCaching #LargeContextModels #Gemini15Pro #AIIntegration

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