Topic
CI/CD Pipelines for LLM Apps
15 posts on ci/cd pipelines for llm apps — part of developer tools on the n4n AI blog.
Setting up GitHub Actions to test prompts on every PR
Set up GitHub Actions prompt testing on pull requests with a practical CI workflow that catches LLM regressions before merge using live model calls.
Secrets management for LLM API keys in CI/CD
Step-by-step playbook for managing LLM API keys in CI/CD: HashiCorp Vault injection, OIDC short-lived creds, and safe fallback patterns for modern pipelines.
Rollback strategies when a model update breaks production
A practical rollback strategy for model update failures in LLM apps: pin versions, canary, automated gates, and fallback to keep production stable.
Parallel eval suites with GitHub Actions matrix jobs
Run parallel eval suites GitHub Actions matrix jobs to shard LLM evaluations across models and cut CI time, with copy-paste YAML and Python.
Monorepo CI for teams shipping multiple LLM-powered features
Practical monorepo CI for LLM features: isolate model calls, mock gateways, run affected tests, and add live smoke tests with fallback.
Linting prompts before they merge with a pre-commit hook
Learn how to build a prompt linting pre-commit hook to catch LLM prompt errors before merge, with step-by-step runnable Python and YAML.
How to keep CI fast when every test hits an LLM API
Practical steps for keeping CI fast with LLM API test calls: mock the client, replay fixtures, use small models, cache layers, and enforce budgets.
Cost budgets in CI: capping token spend on every test run
Learn how to implement capping token spend in CI test runs with a step-by-step guardrail using env vars, usage tracking, and CI config for LLM apps.
Continuous deployment for prompts, without a full release
A practical guide to continuous deployment for prompts: decouple LLM prompts from code, version them, validate in CI, and ship changes without full releases.
CI/CD for LLM apps vs traditional software: what changes
Head-to-head comparison of CI/CD for LLM apps vs traditional software across capabilities, cost, latency, ergonomics, ecosystem, and limits.
Caching LLM API calls in CI to cut test costs
A practical guide to caching LLM API calls in CI pipelines using pytest and GitHub Actions, reducing test spend by replaying deterministic model responses.
Building a CI/CD pipeline for a RAG application
A hands-on tutorial to build a CI/CD pipeline for RAG applications using GitHub Actions, covering indexing, retrieval tests, and automated LLM evals.
Blocking deploys on failed eval scores in GitHub Actions
A hands-on tutorial for blocking deploys on failed LLM eval scores in GitHub Actions: wire evals into CI/CD, fail builds on regressions, ship safer.
A GitHub Actions workflow for n4n-powered apps
Build a practical CI pipeline that tests and smoke-checks LLM apps using n4n.ai's OpenAI-compatible gateway, with GitHub Actions and live fallback validation.
A CI/CD pipeline for LLM apps, step by step
Hands-on guide to building a CI/CD pipeline for LLM apps with GitHub Actions: validate prompts, run eval tests, gate deployments, and ship reliably.
More topics in developer tools
- LLM Evaluation Frameworks15
- Cost Optimization & Model Routing14
- Debugging Hallucinations & Output Quality14
- LangChain Debugging & Observability14
- LLM Observability Platforms14
- Migrating Between LLM Providers14
- Model Deprecation & Version Migration14
- RAG Pipeline Observability14
- Regression Testing for Prompts14
- Compliance & Audit Logging for Regulated Industries13
- CrewAI & AutoGen Multi-Agent Debugging13
- Guardrails & Content Moderation Testing13