Deep engineering guides on the systems I actually build.
Two pillars: hybrid search and RAG retrieval, and reliable queues, workers, and monitoring. Long-form, code-backed, and opinionated — the real tradeoffs, not a definitions dump.
engineering pillars
in-depth guides published so far
practitioner writing, not vendor marketing
Pick the cluster you're working in.
Hybrid Search & RAG
How I design hybrid search and RAG retrieval systems — combining keyword and vector search, reranking, and evaluation so retrieval is precise on identifiers and strong on meaning.
Reliable Systems, Queues & Observability
How I design job queues, worker fleets, and monitoring that stay reliable at scale — idempotency, retries and dead-letter handling, backpressure, and alerting that doesn't cry wolf.
Everything published across both pillars.
Metadata Filtering in Vector Search: Pre-Filter, Post-Filter, and the Recall Cliff
How metadata filtering actually behaves in vector search: why post-filtering breaks tenant isolation, when selective filters collapse ANN recall, and what to do.
Embedding Model Selection for Retrieval: How to Choose Without Trusting a Leaderboard
How I pick an embedding model for retrieval: the constraints that decide it before quality does, a bake-off you can run, and the re-index nobody prices.
Worker Fleet Architecture at Scale: Pools, Scaling Signals, and Draining
Scaling a worker fleet isn't adding workers to one queue. Here's how I partition pools, pick the scaling signal, and drain workers without losing jobs.
How to Evaluate RAG Retrieval: Eval Sets, Metrics, and Ship Gates
Retrieval eval is 20% metrics and 80% eval set. How I build one that survives re-indexing, which number to read at which k, and how to gate a change.
RAG Chunking Strategy: How to Split Documents So Retrieval Works
Chunking decides what your retriever can find. Here's how I split real documents — structure-first boundaries, parent-child units, and how to prove it worked.
Why Is My RAG Retrieval Bad? A Diagnostic Order of Operations
Bad RAG retrieval is four or five distinct failures wearing one costume. Here's how I localize which one you have before changing anything.
Retry with Exponential Backoff and Jitter (and the Retry Budget Nobody Sets)
Exponential backoff caps how fast a client retries; jitter stops every client retrying together. Here's the retry logic I actually ship, and the two limits most teams miss.
Reranking in RAG: How a Cross-Encoder Fixes Retrieval Quality
A cross-encoder reranker re-scores your top candidates by reading query and document together. Here's how I add one, size it, and prove it worked.
Dead Letter Queue Design Patterns (Routing, Envelopes, and Redrive)
A DLQ is the giving-up mechanism, and most teams build it wrong. The routing, envelope, isolation, and redrive patterns I use to make failed messages recoverable.
How to Build Hybrid Search with pgvector and BM25 in Postgres
Build hybrid search in Postgres with pgvector, tsvector, and RRF in one SQL query — the schema, index tuning, and when you actually need real BM25.
Hybrid Search vs Vector Search: Why RAG Retrieval Needs Both
Vector search understands meaning but fumbles exact identifiers; keyword search is the opposite. Here's how I build hybrid retrieval that does both — with fusion, reranking, and a pgvector setup.
How to Design an Idempotent Job Queue (Retries, Backoff, and Dead Letters)
At-least-once delivery makes idempotency mandatory, not optional. Here's how I design job queues that retry safely, back off with jitter, and dead-letter poison messages — with code.
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