#tutorial
6 posts. Other tags: #llm · #evals · #agents · #nodejs · #typescript · #fintech · #healthcare
WhatsApp chatbots, part 1: the setup nobody documents well
Start of a hands-on series on building a WhatsApp chatbot with Meta's Cloud API. Part 1: the account model, creating the app, the test number, your first message from the terminal, and the webhook that receives replies.
Building an LLM feature, part 1: plan before you prompt
A hands-on series on planning, designing and shipping an LLM feature, using the transaction categorizer in my finance app Nora. Part 1: the problem, the success criteria, and deciding where the model belongs.
Building an LLM feature, part 2: design the system around the model
Architecture, data model and the contract with the model for Nora's transaction categorizer: async processing, confidence scores, idempotency and rules that learn.
Building an LLM feature, part 3: build the rules, the model call and the service
The code behind Nora's categorizer: a rules-first decision function, a batched LLM call with a schema-enforced enum, and a background service that saves answers and learns rules.
Building an LLM feature, part 4: test and evaluate it
Unit tests for the deterministic parts, a golden set for the model, and an eval script that reports accuracy, coverage and confident mistakes and can fail CI.
Building an LLM feature, part 5: ship it and keep it healthy
Running Nora's categorizer in production: graceful degradation, timeouts and retries, cost tracking, logs you can query, privacy, and the loop that makes the model less necessary over time.