What is context engineering in LLMs?

It's curating exactly what the model sees—history, docs, tools—in optimal order to avoid garbage outputs.

Does tool calling make LLMs true agents?

Kinda—it lets 'em act via functions, but reliability's still iffy without tight engineering.

How to save costs on LLM apps?

Semantic caching and routing: reuse stable parts, cheap models for easy stuff. Cuts bills 30-50%.

The Dirty 10: LLM Engineering Concepts That Actually Build Working AI

Staring at my coffee-stained notebook from yet another failed AI pitch, I realized: prompts are cute, but these 10 concepts are where the money hides. Forget demos; here's what separates flaky chatbots from enterprise cash cows.

theAIcatchup Apr 07, 2026 3 min read

Infographic breaking down 10 core LLM engineering concepts from context to evals

⚡ Key Takeaways

Context engineering trumps prompt tweaks—order and relevance win. 𝕏
Tool calling and A2A are hot, but multi-agent reliability lags years behind hype. 𝕏
Cash flows to infra: caches, protocols, evals—not shiny demos. 𝕏

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#AI agents #LLM engineering #context engineering #semantic caching #tool calling

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Originally reported by KDnuggets

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