AI Reliability for Data Work: Expert Insights from Data Debug SF

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

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 devs use AI after a query is slow. Better devs use it before writing the query. Instead of: “Optimize this SQL query” Try this: Step 1 — Intent-first prompt “Given this feature, design the most efficient data access pattern with indexes” Now AI thinks in: • access patterns • read/write ratio • index strategy • query shape Not just syntax. Step 2 — Force constraints “Assume 10M+ rows, avoid full table scans, optimize for pagination” This changes the output completely. You’ll get: • composite indexes instead of single ones • cursor-based pagination instead of offset • selective projections (no SELECT *) 💡 Advanced trick: Ask AI to simulate: “Explain how this query executes step-by-step (like query planner)” It will expose: • hidden scans • bad joins • index misses This is insanely useful when you don’t want to rely blindly on EXPLAIN plans. Great performance isn’t about fixing queries. It’s about designing them correctly before they exist. #SystemDesign #Databases #SQLOptimization #PerformanceEngineering #AIAgents #AdvancedCoding #ScalableSystems

Beyond Predictions—The Rise of Agentic Data Science 🤖 In 2026, a model that only "predicts" is officially a legacy model. 📉 For years, the goal of Data Science was to produce an output: a probability, a forecast, or a classification. We handed that number to a human, and the human took action. Today, we are moving toward Agentic Data Science. This isn't just about better models; it’s about models that inhabit autonomous workflows. But here’s the trap: if you build an AI Agent without First-Principles Logic, you’re just automating a disaster. How First Principles guide the "Agentic" shift: 1. Causal over Correlational: An agent taking action needs to understand Cause and Effect. If your model sees ice cream sales and shark attacks rising together, a predictive model might just flag the trend. An agentic model must know that "Summer" is the cause, or it will try to stop shark attacks by banning ice cream. 🍦🚫 2. The "Reward Function" is the new Code: In Agentic systems, you don't just write rules; you define "Success." If your reward function is poorly defined (e.g., "Maximise Clicks"), the agent will find "First Principle" shortcuts you never intended (like clickbait or bots). 3. Small Models, Big Logic: 2026 is the year of SLMs (Small Language Models). We are realising that for specific business tasks, a tiny, specialised model with perfect logic beats a massive, "expensive" model with general knowledge. The First-Principles Strategy for 2026: Stop asking: "How accurate is my prediction?" Start asking: "How robust is the reasoning behind the agent's action?" The tools have shifted from Notebooks to Agents, but the requirement for Clear Thinking has never been higher. What is one task in your workflow that you would trust an AI Agent to handle autonomously today? Let’s talk about the "Trust Threshold" in the comments! 👇 #DataScience #AIAgents #MachineLearning #FirstPrinciples #FutureOfWork #MLOps #MoolaChandanReddy

📉 The gap between data engineering and AI engineering keeps getting smaller with Databricks. Ever wanted to pull structured data from unstructured text using just SQL? Now you can. 💡 With ai_extract(), you simply pass in a string and a state-of-the-art AI function does the heavy lifting. For now in public preview. This part of Databricks is growing suite of AI Functions, bringing LLM capabilities natively into your data workflows. Whether you're extracting emails, names, dates, or custom entities from text at scale, this opens up some powerful possibilities for batch inference workloads. 📰 Doc Link in the comment section. #Databricks #DataEngineering #AIEngineering #GenerativeAI #SQL #LLM Adastra

Curious—are we building for scale first, or jumping straight into AI? Everyone’s talking about AI in data engineering, but here’s the thing—most teams are still struggling with basics like data quality, pipeline reliability, and cost control. AI won’t fix a broken foundation. It amplifies whatever you already have. What stood out to me: - The shift from pipelines → intelligent data products - More automation, less manual orchestration - Engineers evolving into system thinkers, not just coders #dataenginnering #databricks #dataplatform #moderndatastack

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

Everyone is building Text-to-SQL with LLMs. But most systems fail when the database schema becomes complex. QueryWeaver from FalkorDB takes an interesting approach. Instead of relying solely on prompts, it builds a knowledge graph of the database schema — with tables, columns, and relationships as connected nodes. This allows the system to traverse relationships first and generate SQL after, improving accuracy for complex enterprise databases. A nice example of a broader AI pattern: LLMs + structured context (graphs, RAG, memory) → reliable AI systems. Prompting alone isn’t enough. Projects like QueryWeaver hint at what AI-native data infrastructure might look like. #AI #LLM #DataEngineering #GraphDatabase #TextToSQL #OpenSource

QueryWeaver actually started as a demo for FalkorDB. We wanted a quick way to showcase how graph traversal can help LLMs understand complex database schemas. But the response was surprising, many teams building Text-to-SQL are hitting the same problem once schemas get large and interconnected. What began as a simple demo quickly turned into something people were actively looking for. Turns out graphs + LLMs is a very practical combination.

Here’s a problem I see in almost every database team: Most people don’t fully understand their own database schema. Tables grow, columns multiply, and documentation quickly becomes outdated. Soon, no one is 100% sure what each field represents. Lately, I’ve been experimenting with automated schema extraction and metadata analysis. Some interesting challenges: • Handling large schemas efficiently • Generating meaningful column descriptions • Detecting data quality signals (null %, uniqueness, distribution) • Mapping relationships for lineage analysis It’s fascinating how much insight you can get just from metadata. Curious — how does your team maintain schema documentation today? I’d love to hear your approach! #DataEngineering #AI #LLM #Backend #SoftwareEngineering #DataQuality