Practical techniques to cut your LLM cost, survive rate limits, and keep agents lean on real production codebases. Built for engineers shipping with Claude, GPT-5, or Gemini — not for theory readers.
Bill cut in half. Agent that doesn't crash. Context that doesn't grow forever.
01
Prompt Caching — 80% off your bill with one parameter
Cache stable prefixes once, charge 90% less on every read. The three gotchas most people miss (5-min TTL, byte-identical prefix, position).
02
Multi-Model Routing — Stop using Opus for everything
Route by task difficulty: Haiku for grunt work (12× cheaper), Opus for hard reasoning. Real routing patterns and decision rules.
03
Rate Limit Survival — Backoff, Async, Batch
Three patterns: exponential backoff with jitter, async + Semaphore concurrency, and the Batch API at 50% off. When to use each.
04
Don't Feed the LLM Your Repo
ripgrep, Python ast, tree-sitter — narrow context with code-native tools before the LLM sees a single token. 10× cheaper, 10× more accurate retrieval.
05
Tool Pruning & Compaction — Cap the Context
Truncate giant tool outputs to top-N. Auto-compact old turns into one-line summaries. Conversations stay flat-cost forever.
⏳ Coming soon
06
Prompts as Code
Treat your prompts like software. Put them in a repo. Version them with semver tags. PR them through eval gates. Canary-rollout behind feature flags. Roll them back when they break.
⏳ Coming soon
07
Manage Your Context Like a Budget
Four moves for tight context: CLAUDE.md for always-true rules, Skills for sometimes-needed knowledge, subagents for work that would bloat the main window, and compaction when you're nearly full.
⏳ Coming soon
08
Keep Your Context Lean
Context windows don't just run out — they rot. Four practical moves: /context to inspect, trim before you load, /clear vs /compact, and offload state to markdown files on disk. Bonus: sub-agents for free space.
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Engineers and tech leads building agents on top of Claude / GPT / Gemini who are tired of watching their LLM bill double every month and their pipelines crash on rate limits.
Does it apply to OpenAI / Gemini / Bedrock too?
Yes — every technique has provider-specific notes. Caching syntax differs slightly between Anthropic and OpenAI, but the underlying patterns are universal.
Why is it free?
AI Path's mission is making practitioner-grade AI engineering accessible. The book on Kindle and the YouTube course (lessons + breaking AI news) is how you can support the work — no paywall on the techniques themselves.
Updates?
APIs change. Pricing changes. Best-practice changes. Lessons get re-recorded as the ecosystem evolves — re-watch any time.