d2ca02bd90
Restructures section 01 from "web chat / in-editor / agentic" into "web chat vs. tools that live with your code," with the autocomplete / in-project chat / agentic spectrum as a sub-structure of the latter. Inline edits are reduced to a historical note tied to the 2023 instruction-tuned LLM era. - Rename 01-three-modes -> 01-two-worlds and 03-in-editor-workflow -> 03-autocomplete; section 03 narrows to autocomplete (ghost text habits, the autocomplete-your-verification trap) - Section 04 reframes in-project chat as the default venue, web chat as a special-case venue; adds "Carrying context across sessions" covering dev-log.md, CLAUDE.md, .cursorrules - Section 05 reworks intro to contrast against in-project chat instead of "editor extension"; tightens prose and removes em-dashes - Update cross-references and tool-mode language in 02, 06, 07, and the root README to match the new framing - Swap the CRDT example in section 04 for finite-volume methods, fitting the CHEG audience - Minor typo/wording fixes Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
40 lines
3.8 KiB
Markdown
40 lines
3.8 KiB
Markdown
# Coding with AI
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A practical guide to working effectively with AI coding assistants — web chat interfaces (ChatGPT, Claude, Gemini, Microsoft Copilot), in-project chat panels and CLIs (Claude Code, Cursor), autocomplete, and agentic tools. Our focus is on *workflow* and *judgment*: when to reach for which tool, what to paste, how to prompt, how to verify, and what to cite.
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AI tools change quickly, but the patterns change slowly. This guide aims at the patterns and uses current tools as examples.
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**A note on scope.** This guide is about *coding* — writing, editing, refactoring, and debugging software. Students and engineers also use AI tools heavily for *learning* tasks: explaining concepts, summarizing literature, generating practice problems, study quizzes, mnemonics, working through homework, finding the right vocabulary for a half-remembered idea. The web-chat-vs-in-project framing here applies broadly, but the tools, examples, and tradeoffs for learning use cases are different enough to deserve their own guide.
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## Sections
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| # | Topic | Description |
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|---|-------|-------------|
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| [01](01-two-worlds/) | **Two worlds** | Web chat versus tools that live with your code. Why the second is where coding work belongs, and the autocomplete/chat/agent spectrum within it. |
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| [02](02-errors-and-logs/) | **Errors and logs** | The canonical copy-paste case. How to frame what you paste so the assistant can actually help. |
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| [03](03-autocomplete/) | **Autocomplete** | Ghost-text suggestions as you type. What it's good for, the traps (especially in verification code), and when to escalate. |
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| [04](04-conversations/) | **Conversations** | Multi-turn design discussions in the in-project chat or a web chat, managing context, and when to start a fresh chat. |
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| [05](05-agentic-workflow/) | **Agentic workflow** | What agentic tools (Claude Code, Cursor agent, Microsoft Copilot agent mode) actually do, and how to supervise them. |
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| [06](06-verifying-and-citing/) | **Verifying and citing** | Reviewing AI output for hallucinations and silent errors. Privacy and IP of what you paste. Attribution in academic and professional work. |
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| [07](07-local-models/) | **Using local models** | Local models as a cross-cutting alternative — privacy, cost, offline operation. Which tools support local across the autocomplete/chat/agent spectrum, and where the capability gap to cloud still matters. |
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## Who this is for
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Students and practicing engineers who are already using AI assistants but want to use them more deliberately — including those whose default workflow is "ask ChatGPT, copy the answer back." There is nothing wrong with copy-paste, but our goal is to know *when* it is the right tool and when to use something else.
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## Prerequisites
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- A working development setup (editor, terminal, version control). See [computing-setup](https://lem.che.udel.edu/git/furst/computing-setup) and [cli-walkthrough](https://lem.che.udel.edu/git/furst/cli-walkthrough) for the underlying skills.
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- Access to at least one AI tool. The examples use Claude and ChatGPT in web chat form, Claude Code or Cursor as in-project chat / agentic tools, and GitHub Copilot / Codeium / Microsoft Copilot interchangeably for autocomplete. University-provided access (e.g., Microsoft Copilot or Gemini through institutional agreements) works equally well for nearly everything covered here.
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## A note on tools and dates
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Tool capabilities, pricing, and policies change frequently. Where this guide names a specific feature ("Cursor's agent mode," "Claude Code"), the description reflects what those tools did as of the first half of 2026. The underlying patterns — web chat versus tools that live with your code, and the autocomplete/chat/agent spectrum within the latter — are durable. Treat any tool-specific advice as illustrative.
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## License
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MIT
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## Author
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Eric M. Furst, University of Delaware
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