-
Embedding Similarity Search Fails on Domain-Specific Queries — Here's the Architecture Fix
Embedding-based retrieval silently breaks on technical, precise, or domain-specific queries. Here's why it happens and how to architect a hybrid retrieval system that actually works.
-
Debugging RAG Quality Degradation: A Production Troubleshooting Framework
Your RAG system was working fine last month. Now users are complaining about irrelevant answers. Here's the systematic debugging framework to find out exactly what broke — and why.
-
LLM Streaming in Production: Server-Sent Events, Token Buffering, and Handling Mid-Stream Failures
Blocking on a full LLM completion is a UX and infrastructure problem. Here's how to apply SSE and chunked HTTP patterns you already know to stream tokens in real-time — and what breaks when you do it wrong.
-
Handling Hallucinations and Unreliable Outputs in Production LLM Systems
LLMs will hallucinate in production. The question isn't whether it happens — it's whether your system catches it before your users do. Here's a practical, layered approach to detection and mitigation.
-
RAG Chunking Strategy: How Chunk Size, Overlap, and Metadata Shape Retrieval Quality
Chunk size is the highest-leverage dial in your RAG pipeline — and most engineers set it once and forget it. Here's how to tune chunk size, overlap, and metadata extraction to directly improve retrieval precision without rebuilding your entire system.
-
Fine-Tuning vs. Prompt Engineering: A Decision Framework for Backend Engineers
Before you spin up a fine-tuning job, measure whether prompt engineering has actually plateaued. This framework helps you decide when model adaptation earns its operational cost — and when it doesn't.
-
Tokens Are Memory: Context Window Management for Production LLM Systems
Treating context windows as unlimited is the fastest way to blow your LLM budget in production. Learn how to think about tokens as a first-class resource constraint — and build systems that stay within limits without sacrificing quality.
-
OpenAI vs Anthropic in Production: A Backend Engineer's Decision Framework (Not Another Benchmark Post)
Forget the leaderboard scores. Here's how to choose between OpenAI and Anthropic APIs based on what actually matters in production: cost, latency, rate limits, and architectural fit.
-
Vector Databases Demystified: What Backend Engineers Actually Need to Know Before Picking One
Overwhelmed by Pinecone vs. Weaviate vs. pgvector? Most RAG systems don't need a dedicated vector database at all. Here's how to make the right call for your scale.
-
Context Engineering: How to Stop Stuffing Your LLM's Brain and Start Managing It
Modern LLMs have massive context windows, but bigger isn't always better. Learn how to structure and manage context strategically — the discipline engineers are calling 'context engineering.'
-
Embeddings Are Just Coordinates: The Mental Model Every RAG Engineer Needs
Embeddings turn unstructured text into a queryable coordinate system — once you see them that way, RAG retrieval clicks. Here's the mental model, the math you actually need, and how to pick the right model for production.
-
Prompts Are Code: How to Engineer LLM Prompts for Production Systems
Tweaking prompts in ChatGPT until they 'feel right' is the equivalent of testing in production. Here's how to apply the software engineering principles you already know to build reliable, testable, versioned prompts.
-
RAG Is Just a Pipeline. You've Built This Before.
Retrieval-augmented generation sounds intimidating until you realize it's mostly plumbing. Here's how to build your first RAG system without getting paralyzed by vector database decisions.
-
Why You Don't Need ML to Build Your First AI Feature
You don't need to understand backpropagation to ship an AI feature. AI engineering is about integration and composition — and your backend skills already transfer directly.