The AI-First Engineering Team — 30-Day Blog
A 30-day series on what changes when an engineering team — not just an individual engineer — adopts AI as a first-class part of how they work. From culture and workflows to governance and the human side.
Akash Talole
30 Days of AI Engineering — My Content Plan as a Lead AI Engineer
A structured 30-day blog roadmap covering Claude Code, GitHub Copilot, Microsoft Copilot Studio, agentic AI, coding agents, and AI in SDLC — from a Lead AI Engineer with 11 years of experience.
Evaluating RAG Pipelines — The Metrics That Matter
Most RAG systems are deployed without a proper evaluation framework. Teams discover quality problems from user complaints rather than metrics. The evaluation metrics, frameworks, and test set design that make RAG quality measurable.
Chunking Strategies That Actually Work in Production RAG
How you split documents determines what your RAG system can find. Fixed-size chunking is the beginner approach. Semantic chunking, parent-document retrieval, and document-type-aware splitting are what production systems actually use.
Vector Databases in 2026 — Which to Use and When
The vector database landscape has matured. Qdrant, pgvector, Weaviate, and Pinecone each have a clear profile. Here's the decision framework based on scale, existing infrastructure, and query patterns — not benchmarks.
Context Window Management in Production Agents
Context windows are finite even at 100K+ tokens. Long-running agents accumulate state, conversation history, and tool outputs that eventually overflow the window. The strategies that keep production agents working correctly over time.
RAG vs Fine-Tuning — The Hybrid Answer in 2026
The RAG vs fine-tuning debate is largely resolved in 2026: the answer is hybrid. Volatile knowledge in retrieval, stable behaviour in fine-tuning. Here's the decision framework and the specific cases where each approach wins.
RAG in Production — Beyond the Basics
The basic RAG tutorial gets you to a working prototype. Production RAG requires hybrid search, re-ranking, query transformation, and failure handling that tutorials don't cover. What separates a demo from a system that actually works.
The LLM Pricing Collapse — How $0.10/Million Tokens Changes Architecture
LLM inference costs dropped 99.7% between 2023 and 2026. Input tokens that cost $30/million now cost $0.10. This isn't just a budget story — it changes which architectures are economically viable and where to invest engineering effort.
The 13% Problem — Why Enterprise AI Adoption Is Failing
80% of enterprises have adopted AI. Only 13% see enterprise-wide business impact. The gap is not about capability — it's about integration, operational complexity, and the failure to treat AI deployment like production engineering.