#prompt-engineering
6 related articles
·3 min
LoRA Fine-tuning or Prompt Engineering? Two Paths to Solving LLM Long-Conversation Memory
A developer fine-tunes a small model with LoRA to extract conversation state, tackling the LLM long-conversation memory problem. A deep dive into the technical approach, dataset design, and the real trade-offs between fine-tuning and prompt engineering.
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Deep Dives
Deep Dive into AI Agent Skill Design: …
·3 min
Deep Dive into AI Agent Skill Design: Engineering Practices from Anthropic and Perplexity
A deep dive into Skill design philosophy from Anthropic's Claude Code team and Perplexity's Agent team, covering the Tax Test, Gotchas Flywheel, progressive disclosure, and Eval-First practices for building high-quality AI Agent skill systems.
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·6 min
GPT-5.6 Sol Hands-On: How Multi-Agent Parallelism Is Reshaping AI Coding Workflows
Based on Fireship's review, an in-depth look at GPT-5.6 Sol's Ultra Mode multi-agent parallelism, its 91.9% Terminal Bench score, and how it differs from Claude Fable in cost, speed, and precision.
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·7 min
Complete Guide to Installing and Deploying Claude Code: Configure Your AI Coding Tool from Scratch
A detailed walkthrough of the full Claude Code installation process: environment setup, npm installation, proxy configuration for networks in China, API integration, and CC Switch provider management—helping beginners quickly get started with this AI coding tool.
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·2 min
GPT-5.6 Released: How AI Agents Are Moving from the Screen into the Physical World
OpenAI releases the GPT-5.6 model family, focusing on real-world applications: from automating greenhouses and empowering small entrepreneurs, to Codex 5.6 helping a mathematician disprove a three-year problem. A deep dive into GPT-5.6's multi-agent architecture and end-to-end execution.
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·5 min
A Programmer's Guide to Large Language Models: The Complete Path from Prompts to Agents
A tailored large-model learning path for ordinary programmers: from prompt engineering, API calls, and LangChain, to RAG, Agents, fine-tuning, and enterprise deployment—six steps to build AI application skills fast.
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