Technical Analysis Deep Dives into AI Coding Workflows and Engineering Practices

On 2026-11-11, the Chinese developer platform Juejin published a technical breakdown titled "In-Depth Analysis of AI Coding Workflow Details and Engineering Practice." The piece opens by defining AI Coding: an capability in which a large language model takes natural-language instructions, analyzes a codebase automatically, and generates complete code, as opposed to traditional IDE autocomplete that only looks at a few dozen lines near the cursor. The difference is context width. AI Coding must read project structure, dependencies, interface contracts, and tests before producing code that can be merged directly.

A full AI Coding invocation typically goes through four steps: parsing the intent, retrieving context, planning the modification, and generating plus self-checking. The quality of context retrieval determines whether the generated code actually runs, not merely whether it looks plausible. On engineering details, the article points to the temperature value of 0.2 rather than 0.7 as a key choice, since code generation needs determinism rather than creativity. Feedback-looping error messages back into the next round of prompts typically lifts the hit rate from around 50 percent to above 80 percent, which the author calls the most essential difference between AI Coding and one-shot generation. Feeding the model relevant signatures is also said to be more token-efficient and less distracting than supplying entire source files.

On applicability, the article argues AI Coding suits work with clear contracts, such as data transformation, API adaptation, test completion, and documentation generation, while struggling with architectural decisions built on ambiguous requirements and boundary handling that depends on implicit business knowledge. Risks cluster around three points: uncontrolled generated dependencies, since models may invent libraries that do not exist; distorted tests, because a model writing both the implementation and the tests may get both wrong; and context leakage, when private code is sent to external services. The proposed countermeasures are a dependency whitelist, human-written acceptance cases, and local deployment or data masking. The piece concludes that AI Coding should be treated as a pair-programming partner that reads fast and writes steadily but does not understand the business, with the intended workflow being that humans set the contracts, the model fills in the implementation, and tests act as the judge. Based on the available material, no further developments have been reported since.