Blog
Thoughts on AI infrastructure
Engineering deep-dives, product updates, and practical guides for teams building with retrieval-augmented AI.
Why the retrieval step is the real bottleneck in your RAG pipeline
Teams obsess over which LLM to use, then wonder why answers are still wrong. The uncomfortable truth: if the right document never reaches the model, no amount of generation quality can save you.
Vector embeddings explained for non-ML engineers
No linear algebra, no neural network diagrams. Just an intuition for what an embedding actually is, why 'similar things end up close together' is the whole trick, and how that powers semantic search.
Ingestion pipelines that don't fall over: idempotency and fault tolerance
Embedding a document touches a queue, an embedding API, and a vector store, each of which will fail eventually. Here's how to build ingestion that retries safely, never double-writes, and recovers on its own.
What is a knowledge base? (And why your FAQ page doesn't count)
Everyone says they have a knowledge base. Most have a pile of documents with a search box bolted on. Here's the distinction that actually matters once AI is the one doing the reading.
MCP vs. RAG: do you need both?
They get pitched as competitors, but they answer different questions. RAG decides what your model knows; MCP decides what your model can do. Here's how to reason about where each fits, and why serious systems use both.
Getting started with Kognita: ingest your first document in 5 minutes
From zero to a working semantic search over your own content. Create a knowledge base, add a document, and run your first query, all through the API.
Why enterprise AI projects fail at the knowledge layer
The pilot dazzled the boardroom. Six months later the rollout stalled. The failure is almost never the model; it's the unglamorous layer underneath it that nobody budgeted for.
Hybrid search explained: combining BM25 and vector similarity
Full-text search and semantic search each have blind spots. Hybrid search closes the gap. Here is how the scoring works, why RRF solves the score-scale problem, and when to use each mode in production.
Search quality metrics explained: MRR, NDCG, hit rate, and when to use each
You can't improve retrieval you can't measure. Here's a plain-English tour of the four metrics that matter: what each one rewards, what it ignores, and which to reach for depending on how your results are consumed.
Chunking strategies for RAG: fixed-size, semantic, and recursive
The quality of your retrieval is upstream of the quality of your chunks. We benchmarked three common strategies across five document types.
AI knowledge bases for customer support: reducing first response time
Support teams drown in repetitive tickets while answers sit in docs nobody can find fast enough. Here's how an AI-powered knowledge base changes the workflow for both self-service users and the agents behind them.
Migrating to a better embedding model without breaking your product
Switching embedding models invalidates your entire vector index. Here is a zero-downtime migration strategy that keeps retrieval quality stable throughout.
Why every company needs a knowledge base API, not just a knowledge base
A knowledge base humans can browse is table stakes. The companies pulling ahead treat their knowledge as an API, something every app, bot, and agent can query programmatically. Here's why that shift is inevitable.
Building an MCP server backed by a managed knowledge base
The Model Context Protocol lets AI agents pull context on demand. We walk through wiring Kognita's API into an MCP server your models can query directly.
RAG vs fine-tuning: how to choose for your use case
Fine-tuning and retrieval-augmented generation solve different problems. This guide helps you pick the right tool, or use both, for your specific situation.
Kognita is now in early access
After months of building in private, we are opening Kognita to early access teams. Here is what is available today and what is coming next.
Stay in the loop
New posts, straight to your inbox
No noise. We publish when we have something worth saying — usually once or twice a month.
Get in touch to subscribe