AI Engineering Topics
A self-guided outline of common AI engineering subjects.
This page lists topics and examples. It does not provide lessons or a tested curriculum. Check current provider documentation before applying technical details.
Example topics
Language-model concepts
Tokens, embeddings, model inputs, and generated outputs.
Provider APIs
Authentication, requests, responses, and provider documentation.
Prompt structure
Instructions, examples, and output formats.
Streaming and user interfaces
How an application can present partial model responses.
Embeddings and retrieval
Representing text and retrieving relevant records.
Retrieval-augmented generation
Data ingestion, retrieval, context, and source display.
Tool calling
Application-controlled functions and external APIs.
Model selection
Questions to compare across providers and deployment options.
Cost measurement
Token accounting, billing features, and usage limits.
Errors and retries
Timeouts, rate limits, error handling, and retry policies.
Evaluation
Test cases, scoring criteria, and regression checks.
Deployment considerations
Monitoring, secrets, reliability, and operational review.