AI Engineering: Tool use & agents
With tool calling the model can ask your code to run a function (search, query a database, call an API) and then continue with the result.
AI Engineering: Grafana, Prometheus, Tempo & Loki
The Grafana stack is the operations view of your LLM system. Prometheus stores metrics, Tempo stores traces, Loki stores logs, and Grafana puts them on one screen…
AI Engineering: Guardrails, security & cost control
Guardrails are the checks around the model: on what goes in (injection attempts, personal data), on what comes out (schema, grounding, policy), and on what it is…
AI Engineering: Ingestion & chunking
Before anything can be retrieved it must be loaded (turned into clean text), chunked (split into passages small enough to retrieve precisely and send cheaply)…
AI Engineering: Langfuse: LLM tracing & evals
Langfuse is an open-source LLM engineering platform (cloud or self-hosted).
AI Engineering: MCP: the Model Context Protocol
Every AI application wants tools and data: files, databases, tickets, calendars, internal APIs.
AI Engineering: MCP clients & security
An MCP client is the part of a host that connects to a server, lists its tools, and routes the model's tool calls to it.
AI Engineering: Build an MCP server in Python
With the official MCP Python SDK , a server is a set of type-hinted Python functions with decorators.
AI Engineering: OpenTelemetry fundamentals
OpenTelemetry (OTel) is the open standard for producing telemetry: an API your code calls, an SDK that processes the data, a wire protocol ( OTLP ), and a Collector…
AI Engineering: Tracing LLM calls with OpenTelemetry
OpenTelemetry has semantic conventions for generative AI : agreed span names, attribute names and metrics for model calls, retrieval, tool execution and agents.
AI Engineering: Project: an observable docs copilot
Everything in this guide, in one small production-shaped system. docs-copilot indexes every part on this website , answers questions with verified citations …
AI Engineering: Prompt engineering that holds up
A demo prompt is a sentence typed into a playground. A production prompt is a component .
AI Engineering: Evaluating RAG
A RAG system can fail in two places: retrieval did not find the right text, or generation misused the text it was given.
AI Engineering: Build a RAG pipeline in Python
Time to connect the pieces: retrieve, build a prompt with numbered sources, call the model, and return an answer whose citations are checked against what was…
AI Engineering: Retrieval that actually works
'Embed the question, take the top 5' is where RAG starts, not where it ends.
AI Engineering: Sampling, latency & cost
The model hands back a probability for every possible next token. Sampling decides which one is used, and it is why the same prompt can give different answers.
AI Engineering: Tokens, embeddings & the transformer
Inside the model, text goes through four steps. It is split into tokens (integers), each token becomes a vector , a stack of transformer layers lets every token…
AI Engineering: Embeddings & vector databases
An embedding model turns a passage into a vector so that passages with similar meaning point in similar directions.
AI Engineering: What an LLM actually is
A large language model is a function with one job: given some text, predict the next token (a word or a piece of a word).
AI Engineering: Why LLM apps need observability
A classic web service is healthy if it is fast and returns no errors. An LLM application can be fast, error-free and wrong : it answers from the wrong document…
AI Engineering: Why RAG exists
Retrieval-augmented generation (RAG) means: before asking the model, search your documents for the passages relevant to the question, put those passages in the…