Insights
AI & Machine Learning
Agents, LLM pipelines, RAG, forecasting and computer vision — how they are built and what breaks in production.
AI & Machine Learning
AI & Machine Learning ·
RAG, Fine-Tuning or Long Context: How We Choose
Start with the simplest setup that passes an eval on your own questions: a cached long prompt for a small, stable corpus, RAG when knowledge changes, must be cited or is access-restricted, and fine-tuning only for behaviour. Here is the order we check things in and where each option breaks.
9 min read
AI & Machine Learning ·
Why Hybrid Search Beat Pure Embeddings in Our Invoice AI Pipeline
Pure dense embeddings failed on alphanumeric serial numbers in our Invoice AI pipeline. Combining PostgreSQL tsvector full-text search with pgvector cosine distance lifted top-1 matching accuracy from 61.4% to 94.8%.
6 min read
AI & Machine Learning ·
Why pgvector Replaced Qdrant in Our LLM Feedback Pipeline
We migrated 5 million vector embeddings from a standalone Qdrant cluster to PostgreSQL 16 with pgvector 0.7. Here is how operational overhead dropped while maintaining sub-50 ms search latencies.
5 min read
AI & Machine Learning ·
How AI agents work: the architecture of a production agent
An agent is a model wrapped in perception, memory, tools, orchestration and monitoring. A practical walk through the six layers, what breaks in each one, and what we do about it.
6 min read
AI & Machine Learning ·
RAG in production: the checklist we run before go-live
Retrieval-augmented generation demos in a day and fails in a month. The twelve checks — data, retrieval, generation, operations — that separate a pilot from a system people rely on.
5 min read