[2/2] Prompt Engineering for LLMs (AI column)
Finishing. story about a book about industrial engineering, you should talk about advanced topics, and also share your thoughts about the relevance of the book in 2026 year.
3ви Advanced topics: development of chat agents with memory and tools (method)Breaking down complex tasks into steps (workflows)Evaluation of the quality of decisions by the model. 8. Conversational Agency Tool Use in Details: How to Design Tools (names/arguments)Resolve errors and dangerous actions; reasoning patterns (CoT, ReAct and beyond); context for task dialogues; agent assembly, dialogue management, and UX. 9. LLM Workflows - when a "chat agent" is not needed, but workflow is needed: tasks like bricks; implementation through template bumps or tools; complication / variation; the idea of "eval begins at the task level"; an example of end-to-end workflow; advanced schemes: agent-driven workflow, stateful task agents, roles / delegation. 10. Evaluating LLM Applications Offline: example suites, gold standard / functional testing / LLM-as-judge, SOMA; online: A/B and metrics. 11. Looking Ahead - where everything goes: multimodality, UI/UX as part of quality, growth of "intelligence" / speed / availability of models.
In general, there is a lot of content in the book, but the question arises as to how useful it is now. (into 2026 year). And indeed, since the release of the book, LLM-technologies have stepped forward, and some of them have become more and more successful. 2025He said, “Prompt engineering is dead.” The quality of the models has grown – they understand the user better even without clever hints; in addition, the best techniques are already built into the tools. (ready-made). In real products, the behavior of the model is increasingly determined by system settings or fine tuning, rather than the text of the user query. As a result, the role of “prompt engineer” soon merged with the broader roles of AI/LLM developers.
However, the book has not lost its value - its fundamental principles are still useful to every engineer. The authors frankly warned that specific APIs could become obsolete, but the basic ideas would remain relevant. So it happened: for example, the RAG approach is now widely used to connect knowledge models, and the technology chain-of-thought has become a standard technique in modern AI-agents. And 2025 Andrey Karpathi coined the term “context engineering” – a focus on providing a full environment model. (data, history, tools) Instead of the perfect wording of the request. This approach requires the ability to dynamically find and substitute the necessary information, compress them under the context window, clearly define roles. (system/user) Connect external tools as needed. In addition, there were practices like PromptOps - management of prompts. (Versioning, monitoring the quality of requests). And some teams are already trying to fully automate the preparation of context and instructions. (workflow approach).
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