Project Management

Most people do not look beyond the basic RAG pipeline, and it rarely works out as expected! RAG is known to lack robustness due to the LLM weaknesses, but it doesn't mean we cannot build robust pipelines! Here is how we can improve them. The RAG pipeline, in its simplest form, is composed of a retriever and a generator. The user question is used to retrieve the database data that could be used as context to answer the question better. The retrieved data is used as context in a prompt for an LLM to answer the question. Instead of using the original user question as a query to the database, it is typical to rewrite the question for optimized retrieval. Instead of blindly returning the answer to the user, we better assess the generated answer. That is the idea behind Self-RAG. We can check for hallucinations and relevance to the question. If the model hallucinates, we are going to try again the generation, and if the answer doesn't address the question, we are going to restart the retrieval by rewriting the query. If the answer passes the validation, we can return it to the user. It might be better to provide feedback for the new retrieval and the new generation to be performed in a more educated manner. In the case we have too many iterations, we are going to assume that we just reach a state where the model will apologize for not being able to provide an answer to the question. When we are retrieving the documents, we are likely retrieving irrelevant documents, so it could be a good idea to filter only the relevant ones before providing them to the generator. Once the documents are filtered, it is likely that a lot of the information contained in the documents is irrelevant, so it is also good to extract only what could be useful to answer the question from the documents. This way, the generator will only see relevant information to answer the question. The assumption in typical RAG is that the question will be about the data stored in the database, but this is a very rigid assumption. We can use the idea behind Adaptive-RAG, where we are going to assess the question first and route to a datastore RAG, a websearch or a simple LLM. It is possible that we realize that none of the documents are actually relevant to the question, and we better reroute the question back to the web search. That is part of the idea behind Corrective RAG. If we reach the maximum of web search retries, we can give up and apologize to the user. Here is how I implemented this pipeline with LangGraph: https://lnkd.in/g8AAF7Fw

Don't fall victim to shiny tech syndrome. Invest in solutions addressing critical workflow challenges. Here's are three suggestions for how: 1) Document your team's three most significant operational bottlenecks with measurable data points. Resist the urge to solve theoretical problems—focus on documented friction that impacts performance daily. 2) Vendors may like me saying this (and, full disclosure, I work for one), but don't rely solely on vendor-supplied ROI projections. Construct evaluation criteria specifically calibrated to your team's unique pressure points and operational realities. 3) Develop a plan for integrating a potential new tool into your existing toolbox. Map intersection points with existing systems and cultivate internal advocates positioned to drive meaningful adoption across the enterprise or specific business area. #innovation #law #business #learning

I’ve spent the last year building and experiencing successful LLM use cases, and here’s where I’ve seen them create real impact: When LLM-based capabilities are embedded inside existing workflows. Chatbots are a great starting point, but here is how you can take them one step further: 1. Contract analysis that understands clauses, redlines, and legal context, reducing hours of manual review. 2. Co-pilots for internal tools, from writing SQL queries and generating Jira tickets to even composing HR emails, all inside your existing platforms. This is the shift: from flashy demos to practical, embedded intelligence that drives outcomes. P.S.: This is my 38th post in the '100 days of LLMs' series. Follow along to join this journey. #data #ai

I've put my last 6 months building and selling AI Agents I've finally have "What to Use Framework" LLMs → You need fast, simple text generation or basic Q&A → Content doesn't require real-time or specialized data → Budget and complexity need to stay minimal → Use case: Customer FAQs, email templates, basic content creation RAG: → You need accurate answers from your company's knowledge base → Information changes frequently and must stay current → Domain expertise is critical but scope is well-defined → Use case: Employee handbooks, product documentation, compliance queries AI Agents → Tasks require multiple steps and decision-making → You need integration with existing tools and databases → Workflows involve reasoning, planning, and memory → Use case: Sales pipeline management, IT support tickets, data analysis Agentic AI → Multiple specialized functions must work together → Scale demands coordination across different systems → Real-time collaboration between AI capabilities is essential → Use case: Supply chain optimization, smart factory operations, financial trading My Take: Most companies jump straight to complex agentic systems when a simple RAG setup would solve 80% of their problems. Start simple, prove value, then scale complexity. Take a Crawl, Walk, Run approach with AI I've seen more AI projects fail from over-engineering than under-engineering. Match your architecture to your actual business complexity, not your ambitions. P.S. If you're looking for right solutions, DM me - I answer all valid DMs 👋 .

8 out of 10 businesses are missing out on Ai. I see this everyday in my calls. They jump straight to AI tools without understanding their processes first. Then wonder why their "automations" create more problems than they solve. Here's the proven framework that actually works: STEP 1: MAP YOUR PROCESSES FIRST Never automate a broken process. → List every touchpoint in your workflow → Identify bottlenecks and time-wasters → Note who handles each step → Find communication gaps Remember: You can only automate what you understand. STEP 2: START WITH HIGH-ROI TASKS Don't automate because it's trendy. Focus on what saves the most time: → Data entry between systems → Client onboarding workflows → Report generation → Follow-up sequences One good automation beats 10 fancy tools that don't work together. STEP 3: BUILD YOUR TECH FOUNDATION Most companies use 10+ disconnected tools. AI can't help if your data is scattered everywhere. → Centralize data in one source (Airtable works great) → Connect your core systems first → Then layer AI on top STEP 4: DESIGN AI AGENTS FOR SPECIFIC PROBLEMS Generic AI = Generic results. Build precise agents for precise problems: → Research and data analysis → Customer support responses → Content creation workflows → Internal process optimization Each agent needs specific inputs and defined outputs. STEP 5: TEST SMALL, SCALE SMART Don't automate your entire business at once. → Start with one small process → Get team feedback → Fix bottlenecks as you go → Scale what works Build WITH your team, not without them. The biggest mistake I see? Companies hire someone to build exactly what they ask for. Instead of finding someone who challenges their thinking and reveals what they're missing. Good automation is just process optimization. Nothing more. The result? → 30+ hours saved per month on onboarding → Delivery time cut in half → Capacity increased by 30% → Revenue multiplied without adding team members Your competitors are stuck switching between apps. You'll be dominating with seamless systems. Follow me Luke Pierce for more content on AI systems that actually work.

🚀 Understanding the MCP Workflow: How AI + Tools Work Together Seamlessly In today’s fast-evolving AI landscape, it's not enough for a model to simply generate text — it must be able to take action, access tools, and interact with real-world systems. That’s exactly what the MCP (Modular Control Plane) Workflow enables. This visual outlines a powerful architecture that connects LLMs with the real world through a smart orchestration layer. 🔍 Let’s break down how it works: 1️⃣ Prompt Ingestion – It all begins with a user prompt. 2️⃣ Tool Discovery – The MCP Host fetches the right metadata about all available tools from the MCP Server. 3️⃣ Planning Phase – The client sends a structured combination of prompt and tool metadata to the LLM, letting it reason and select the best tool. 4️⃣ Tool Execution – Specific tools (code, APIs, DBs, etc.) are invoked by the client. 5️⃣ Context Update – The result from the tool is sent back with the prompt to maintain continuity. 6️⃣ LLM Final Output – A smart, fully informed response is generated and delivered to the user. ⚙️ The connected components include: ✅ GitHub Repos ✅ Databases ✅ APIs ✅ Custom tools (N number of them!) 💡 This system is the backbone of AI agents, enabling them to behave less like static chatbots and more like autonomous operators. 📌 Whether you're working on AI copilots, internal automation, or intelligent task runners — this structure gives you the clarity and control needed to scale. Imagine your AI not just talking, but coding, querying, fetching, building, and solving — all autonomously. This is the kind of workflow that makes that vision real. 🔥 The future of intelligent systems is not just generative. It's interactive, tool-augmented, and goal-oriented.

Stop asking "What's the best AI coding tool?" Start asking "What am I trying to accomplish?" Dan Olsen created the excellent "Vibe Coding Spectrum" below, mapping most tools on technical complexity & way to use them. Building on that, and after extensive internal use, running hackathons, and having taught AI Coding at universities in both continents, I wanted to share my quick framework for tool selection: For Visual Prototyping (Speed Priority): - Magic Patterns: Consistent design systems, copy components, open canvas & varied defaults - Lovable: Non-technical friendly with best visual off the bat, and a great balance of integrations + ease of use - Free alts: Check out 'Deepsite' for quick free demos For Functional Applications (Completeness Priority): - v0: Tightest stack (They created Next.js and hired the shadcn dev), and most integrations. Super easy to add AI backends (check out their v5 SDK!) - Replit: Full-stack with integrated database, takes longer per generation. Need to be a little technical to get the most from it For Production Development (Control Priority): - Cursor: My go-to. Advanced context management, production-ready workflows. Although WE ARE ALL confused about their pricing. - Windsurf / Copilot: Alternative with competitive feature set, getting there. - Claude Code / Codex: CLI alternative. Claude models have generally been better for development, but GPT-5 is now preferred by some. In short » These tools are converging on features but diverging on workflow optimization. Choose based on your primary objective -> speed, completeness, or control? Most successful teams use 2-3 tools in sequence: prototype quickly, validate with users, then transition to production-grade development. ---- Our AI Dev Stack at Zentrik?  1) Explore with Magic Patterns or v0 -> Send out and gather input. 2) Load that context into Zentrik & organize & prioritize our work. 3) Cursor 20/mo + Claude Code (200/mo) for \engineering work. What about you? What approach aligns more with your real needs?

Raise your hand 🙋🏻♀️ if this has ever happened to you ⤵ You put a piece of content in front of someone for approval. They say, “You should show this to Sally. She’d have thoughts on this.” So you show it to Sally. She not only has thoughts, but she also recommends you share the draft with Doug. Doug also has feedback, some of which aligns with Sally’s and some of which does not. Now you’re two days behind schedule, have conflicting feedback to parse through, and are wondering how you could have avoided this mess. Try this next time 👇 In the planning phase of a project, put a doc together that outlines 3 levels of stakeholders: 1) Your SMEs 🧠 → Apply as much of their feedback as possible — they are as close a proxy to your audience as you can get. 2) Your key approver(s) ✅ → Keep this group small, 1–2 people if possible. → Weigh their feedback knowing that they are not necessarily an SME 𝘣𝘶𝘵 they do control whether or not the project moves forward. 3) Your informed partners 🤝 → Typically, those who will repurpose or promote your content in some way. (e.g. field marketing, comms, growth, etc.) → Make revisions based on their feedback at your discretion. → You may even want to frame the delivery of your draft as, "Here’s an update on how this is progressing. No action needed at this time." Share this doc with all listed stakeholders. Make sure they understand the level of feedback you’re expecting from them, and by when. Then use the doc to track feedback and approvals throughout the life of the project. Preventing your circle of approvers from becoming concentric: 👍 keeps you on track 👍 keeps your content from pleasing your stakeholders more than your audience

Good article on lessons learned with RAG. IMHO RAG will continue to be dominant architecture even with long context LLMs. 1) Modular Design > Big Monoliths: Success in RAG relies less on fancy models and more on thoughtful design, clean data, and constant iteration. The most effective RAG pipelines are built for change, with each component (retriever, vector store, LLM) being modular and easy to swap. This is achieved through interface discipline, exposing components via configuration files (like pipeline_config.yaml) rather than hardcoded logic 2.Smarter Retrieval Wins: While hybrid search (combining dense vectors and sparse methods) is considered fundamental, smarter retrieval goes further6. This includes layering in rerankers (like Cohere’s Rerank-3) to reorder noisy results based on semantic relevance, ensuring the final prompt includes what matters. Source filters and metadata tags help scope queries to relevant documents. Sentence-level chunking with context windows (retrieving surrounding sentences) reduces fragmented answers and helps the LLM reason better. Good retrieval is about finding the right information, avoiding the wrong, and ordering it correctly 3.Build Guardrails For Graceful Failure: Modern RAG systems improve upon early versions by knowing when not to answer to prevent hallucination7.... Guardrails involve using system prompts, routing logic, and fallback messaging to enforce topic boundaries and reject off-topic queries. 4. Keep Your Data Fresh (and Filtered): The performance of RAG systems is directly tied to data quality. This means continuously refining the knowledge base by keeping it clean, current, and relevant. Small changes like adding UI source filters (e.g., limiting queries to specific document types) resulted in measurable improvements in hit rate. Monitoring missed queries and fallbacks helps fill knowledge gaps. Practices like de-duping files, stripping bloat, boosting trusted sources, and tailoring chunking based on content type are effective. Data should be treated like a product component: kept live, structured, and responsive. 5.Evaluation Matters More Than Ever: Standard model metrics are insufficient; custom evaluations are essential for RAG systems. Key metrics include Retrieval precision (Hit Rate, MRR), Faithfulness to context, and Hallucination rates. Synthetic queries are useful for rapid iteration, validated by real user feedback. Short, continuous evaluation loops after every pipeline tweak are most effective for catching regressions and focusing on performance improvements. https://lnkd.in/gkXgJvEY

RAG systems are failing most companies. Here's why and 3 ways to fix it. I've been researching RAG optimization for businesses processing hundreds of files daily. The problem? Basic vector search is too weak. It retrieves irrelevant chunks. Misses context. Struggles with large datasets. Most companies are doing this wrong: They dump everything into a vector database and hope for the best. That's like throwing darts blindfolded. Guys from LlamaIndex (leading  data orchestration framework) shared what actually works: 📌 Strategy 1: Context Expansion - Don't pull just one vector chunk. - Pull 2 chunks before and 2 chunks after. - Think of it like reading a book — you need surrounding sentences to understand meaning. Pro tip: Use AI to validate if the expanded context helps. If not, trim it. 📌 Strategy 2: Small to Big Search Two-step process: Step 1: Search metadata summaries first Step 2: Retrieve actual content from filtered sections Instead of searching raw text, you search organized summaries. Like having a smart librarian who knows exactly which shelf to check. 📌 Strategy 3: Multi-Agent Breakdown - Break complex queries into sub-questions. - Different agents handle different pieces. - Results get combined for comprehensive answers. I created an N8N workflow that applied all 3 approaches, and the results of searching through 5,000 vectors were amazing! Should I share it?