Agentic Data Science: Beyond Predictions

Is your database smart enough to understand "meaning"? 🧠 Standard databases are great at matching keywords, but they’re "blind" to context. If you search for "Emerald City," a traditional DB looks for those exact words. A Vector Database understands you're probably looking for "The Wizard of Oz" or "Seattle." Why does this matter in 2026? Because Vector DBs are the secret sauce behind modern AI. They act as the "External RAM" for LLMs, allowing companies to: ✅ Stop AI hallucinations by providing real-time context (RAG). ✅ Build recommendation engines that actually understand user "vibes." ✅ Search through millions of images or videos in milliseconds. Whether you're using Pinecone, Weaviate, or Chroma, if you aren't thinking about vector embeddings, you're leaving the "intelligence" out of your data. Are you implementing Vector Search this year, or sticking to traditional SQL? Let's discuss in the comments! 👇 #AI #VectorDatabase #MachineLearning #DataScience #SoftwareEngineering #TechTrends2026

2026: Beyond Just Prompting – The Real Work That Keeps You Relevant🪄 We’re years into the LLM revolution, and yes, models are smarter than ever. But if you think just plugging in GPT-5 or Claude-4 guarantees success🫠 ...then then then... you’re missing the point. The real differentiator today isn’t the model – it’s how you wield it💪 Here’s what separates those who stay relevant from those who fade: · Deep data understanding – Not just schema, but semantics. How do your users describe things vs. how they’re stored? · Strategy & planning – When to retrieve, when to abstain, when to ask for clarification. · Versioning & rollback – Your agent’s logic will evolve. Can you revert a broken prompt or tool change in seconds? · Proper testing – Unit tests for SQL generation, regression tests for RAG pipelines. One “hallucinated” join can cost a client. Take a SQL query generator. It’s not enough to translate “happy mind” into SQL. · Which columns should be tried first? · When do you fall back to fuzzy matching vs. asking the user? · How do you prevent “crispy mind” from polluting results when only “happy mind” was meant? Or RAG: · How do you chunk documents based on user intent, not just token limits? · What’s your re-ranking strategy when the top‑5 chunks miss the key fact? · How do you version your embedding model without breaking existing retrievals? These aren’t “minor” details. They’re the engineering backbone that makes AI reliable. In 2026, the hype has settled. The winners are those who obsess over the boring stuff – because that’s where value is built🌱 #AI #MachineLearning #DataEngineering #LLMs #RAG #SoftwareEngineering

How is RAG different from LLM ?? How is it able to provide company specific context ?? Heard anything about vector platforms like Pinecone and ChromaD ?? If No, this short video will give you insight on Vector Database ? Traditional SQL databases often fail because they require exact keyword matches; for example, if an employee searches for "clothing" but the policy is titled "dress code," the system returns zero results,. Vector Databases solve this by bridging the "semantic gap" between how humans ask questions and how computers store data,. Here is why they are the backbone of modern AI: 🧠 Semantic Search: They understand the intent and context of a query rather than just matching characters. 🔢 Embeddings: They turn text into "embeddings"—long lists of numbers (vectors) that represent the actual meaning of words,. 📐 Dimensionality: They use hundreds of dimensions to capture complex nuances like tone, formality, and topic,. ⚡ Efficiency at Scale: They use smart indexing and hashing to search through millions of records in milliseconds,. Check out this video I created using NotebookLM to see how Vector Databases make AI smarter and more intuitive! 🎥👇 #VectorDatabase #AgenticAI #genAI #SemanticSearch #NotebookLM #DataScience

Most people still think RAG = vector search + LLM answer. But the system Databricks described with their agent KARL hints at something more interesting. The real shift isn’t better retrieval. It’s teaching the model how to search. Typical RAG looks like this: User query → vector search → retrieve top-k documents → send to LLM → answer Simple pipeline. KARL changes the flow completely. Instead of one retrieval step, the model runs a reasoning loop: Query → generate search plan → retrieve documents → evaluate results → refine query → search again → compress context → reason on evidence Sometimes this loop runs 100+ searches before producing an answer. So the model isn’t just answering questions. It’s figuring out how to find the answer. That’s a very different problem. Another interesting detail: context compression is part of the reasoning process. In most RAG systems, if you retrieve too much information you just: • rerank • prune chunks • summarize KARL instead trains the agent to compress its own working memory while it reasons. Remove that step and accuracy dropped from 57% → 39% on their benchmark. Which suggests something important: Memory management might actually be part of reasoning, not just infrastructure. That said, the architecture still has some clear limits. Right now it seems heavily built around vector retrieval. But real enterprise systems usually need a mix of: • vector search • SQL queries • graph traversal • APIs • structured data Without those tools, even a smart search policy hits a ceiling. Still, the bigger takeaway isn’t the specific system. It’s what direction things are moving in. AI systems seem to be evolving like this: Phase 1 RAG chatbots Phase 2 Agentic RAG Phase 3 Search-native AI systems KARL feels like a step between phase 2 and phase 3. And if that trend holds, the real competition in AI might shift from who has the best LLM to who trains the best search strategy models. Because the hardest part was never generating text. It was knowing where to look for the truth. Curious how others building enterprise AI systems are thinking about this. #AIArchitecture #AgenticAI #RAG #EnterpriseAI #MachineLearning

Invalid Values: The Silent Risk in Your Data Let’s talk about something you’ve definitely seen in your data. An invalid value is any data point that does not match what you expect. It could be the wrong type, the wrong format, or something that just doesn’t make logical sense. You might run into: • A text value sitting in a numeric column • A date like “February 30” • A negative age • A customer ID that doesn’t follow your defined pattern Why should you care? Because these small issues can cause bigger problems than you think. They can: • Break your calculations • Create errors in downstream systems • Lead you to insights that are simply wrong So what can you do about it? Here are a few practical ways you can handle invalid values: 1. Validation rules Set up checks like data types, formats, or regular expressions (regex) so you can catch issues early 2. Correction If you have enough context, you can fix the value using logic or other fields 3. Rejection If you cannot confidently fix it and it is a small portion, you can remove it 4. Flagging When in doubt, flag it and review it instead of guessing At the end of the day, clean data is not just about being correct. It is about making sure you can trust what you are working with. How do you usually deal with invalid values in your workflow? Let me know in the comment section. #DataScience #MachineLearning #ArtificialIntelligence #Statistics #Geospatial #RemoteSensing #UrbanPlanning #AI #DataAnalytics

Day 2 of 150. The mission to build an AI Data Agency isn’t about flashy prompts. It’s about architecture. Today was a deep dive into SQL Foundations and Database Design. Most "AI experts" jump straight to the LLM. But if the underlying data isn't structured, the AI is just guessing. I’m building systems that don't just "chat"—they calculate and scale. Today’s Milestone: •Practicing and Writing 20+ beginner level queries from memory to ensure the logic is hardcoded into my brain. •Understood the basic logic behind the language  Structured data is the "Intelligence Explosion" fuel. If you aren't governing your data, you aren't ready for AI. Onward. 🇳🇬 #DataAnalytics #SQL #AI #BuildInPublic #Day2

Data Debug brought three practitioners to Mux's office this past Tuesday to answer one question: how do you make AI actually reliable for data work? The talks told a complete story. Claire Gouze CEO/Founder at nao Labs (YC X25) benchmarked 21 AI analytics tools on text-to-SQL accuracy. The headline finding: going from no context to a cleaned data model jumped accuracy from 17% to 86%. Semantic layers alone? 4% correct. Context quality is everything. Our own Dori Wilson shared the AI skills framework she built to operationalize that context. Skills are markdown files that encode domain knowledge, workflows, and guardrails into AI coding tools. Structured as a self-improving loop, every session compounds. She walked through a real aggregation bug Claude introduced, how a review skill caught it, and how the fix became a permanent rule the system enforces automatically. Kasia Rachuta (Lead Data Scientist) showed the breadth of what's possible today: analyzing CS tickets with Snowflake Cortex AI, fuzzy address matching that beat regex by 20%, automated Slack responses from documentation, and ETL doc generation. The practical filter: knowing when AI saves time versus when it's faster to write the code yourself. All three full talks are now on YouTube. See them here: https://lnkd.in/g6f_TxSP Data Debug SF runs monthly. If you're building with AI in data, this is the room to be in. #DataDebugSF #DataEngineering #AnalyticsEngineering #AI #dbt

Vector Databases are failing your complex data. Here’s why. 🛑 I only have 1,608 followers, but I’ve spent months building Agentic AI systems. One thing is clear: Semantic search (Vector RAG) is hitting a ceiling. If you want to lead in 2026, you need to move beyond "Similarity" and toward "Relationships." Here is the 5-step system for Vector-less (Graph-based) RAG: Step 1: Stop Chunking, Start Extracting. Traditional RAG breaks text into random chunks. Vector-less RAG extracts Entities and Relationships. * Use LLMs to identify "Who," "What," and "How" they connect. Step 2: Build the Knowledge Graph. Instead of a flat vector space, map your data into a Graph Database (like Neo4j). Nodes = Concepts. Edges = Logic. Step 3: Follow the "Global-to-Local" Retrieval. Vectors are great for "Local" facts. Graphs are kings of "Global" context. Ask: "What are the common themes across 1,000 documents?" A vector search will hallucinate; a Graph RAG will summarize. Step 4: Use the "Reasoning Path" Hook. Don't just retrieve; Traverse. * Your Agent should follow the edges of the graph to find non-obvious connections that a simple similarity search would miss. Step 5: Score Your Connectivity. If your retrieval doesn't capture the "Relationship" between data points, your RAG is just a fancy CTRL+F. [ 🔖 Save this post—Vector-less RAG is the competitive edge for 2026 ] At SkilliHire, we are moving past the "Prompt Hobbyist" phase. We are building production-ready AI Architectures that actually handle complex enterprise data. The "AI Gap" is real. Don't get left behind using 2023 tech. Explore the future of Agentic AI at www.skillihire.com. Are you still relying on Top-K vector retrieval, or have you started building Knowledge Graphs? Let’s debate in the comments. 👇 #RAG #GraphRAG #AIArchitect #SkilliHire #AgenticAI #MachineLearning #MLOps #AdeelHamid

Vector Databases are failing your complex data. Here’s why. 🛑 I only have 1,608 followers, but I’ve spent months building Agentic AI systems. One thing is clear: Semantic search (Vector RAG) is hitting a ceiling. If you want to lead in 2026, you need to move beyond "Similarity" and toward "Relationships." Here is the 5-step system for Vector-less (Graph-based) RAG: Step 1: Stop Chunking, Start Extracting. Traditional RAG breaks text into random chunks. Vector-less RAG extracts Entities and Relationships. * Use LLMs to identify "Who," "What," and "How" they connect. Step 2: Build the Knowledge Graph. Instead of a flat vector space, map your data into a Graph Database (like Neo4j). Nodes = Concepts. Edges = Logic. Step 3: Follow the "Global-to-Local" Retrieval. Vectors are great for "Local" facts. Graphs are kings of "Global" context. Ask: "What are the common themes across 1,000 documents?" A vector search will hallucinate; a Graph RAG will summarize. Step 4: Use the "Reasoning Path" Hook. Don't just retrieve; Traverse. * Your Agent should follow the edges of the graph to find non-obvious connections that a simple similarity search would miss. Step 5: Score Your Connectivity. If your retrieval doesn't capture the "Relationship" between data points, your RAG is just a fancy CTRL+F. [ 🔖 Save this post—Vector-less RAG is the competitive edge for 2026 ] At SkilliHire, we are moving past the "Prompt Hobbyist" phase. We are building production-ready AI Architectures that actually handle complex enterprise data. The "AI Gap" is real. Don't get left behind using 2023 tech. Explore the future of Agentic AI at www.skillihire.com. Are you still relying on Top-K vector retrieval, or have you started building Knowledge Graphs? Let’s debate in the comments. 👇 #RAG #GraphRAG #AIArchitect #SkilliHire #AgenticAI #MachineLearning #MLOps #AdeelHamid

🚀 AetherOS v1.1: It finally feels like a real “second brain” Been working on my personal AI system (AetherOS), and honestly… this update changed everything. Earlier it was just: “store notes → search → hope something useful comes back” Now it actually understands how my notes are structured. 🧠 What I upgraded I rebuilt the ingestion pipeline from scratch — not fancy, just done properly: Hierarchical chunking (H1 → H2 → H3) → Now it retrieves sections, not random text Parent–child linking → If it finds a small detail, it can expand to the full context 20% overlap → No more missing important lines in the middle Rich metadata (file, tags, timestamp, heading path) → I can filter like: “only backend notes from recent work” Stable IDs (no duplicates) → Re-indexing doesn’t break things anymore Clean re-sync system → Edit a file → old data gone → fresh data in (no ghost chunks) Dense + Sparse vectors ready → Preparing for hybrid search (this is next) 📊 The difference is real Before: Results felt random sometimes Good info was buried Context was messy Now: Answers are actually relevant It pulls the right section It feels like my notes are being understood, not just searched Accuracy jumped from ~60% → almost 90%+ 🧩 The biggest realization The real power isn’t embeddings. It’s: Structure + Metadata + Retrieval logic Most people skip this part… but this is where everything changes. 🚧 Next step Now that the data layer is solid, I’m moving to: Hybrid search (semantic + keyword) Reranking Context reconstruction Basically making it think better, not just store better. 💭 Final thought This is the first time my system feels less like a tool… and more like something that actually remembers things the way I do. If you're building RAG systems or second-brain tools — don’t just focus on models. Focus on how your knowledge is structured. That’s the real upgrade. #AI #RAG #SecondBrain #BuildInPublic #Engineering #LLM