# PR Review

### Code Review

#### 1. **Summary of what changed and its intent**
The primary change involves the introduction of new functions in `llm/prompts.py` for generating prompts related to code review processes: `generate_analyze_diff_prompt`, `generate_small_change_prompt`, and `generate_big_change_prompt`. These are designed to standardize and centralize the prompt generation for the LLM calls, improving maintainability, readability, and consistency across the codebase. The prompts replace hard-coded strings directly within the functions in `llm/openai.py`, which is a move towards better modularization and separation of concerns.

#### 2. **Breaking changes** — are callers still compatible?
No breaking changes were introduced by these modifications. The calling functions (`llm_analyze_diff_size`, `llm_review_small_change`, and `llm_review_big_change`) still utilize the same input parameters and return types. The public API remains consistent, meaning that other parts of the codebase that interact with these functions will not be impacted directly. The reflection on prompts has been encapsulated, making it easier to manage without altering the usage or input/output contract.

#### 3. **Impact analysis** — how each impacted file is affected
The changes primarily impact the `llm/prompts.py` and `llm/openai.py`. The modularized prompt generation functions:
- Enhance the clarity of prompt-related logic.
- Enable future text updates to be made in one place, reducing overhead in search-and-replace across multiple functions.
- The core functioning of `llm_analyze_diff_size`, `llm_review_small_change`, and `llm_review_big_change` remains intact as no existing logic was altered; they simply replaced prompt creation logic.

#### 4. **Bugs / edge cases** in the changed code
- The `json.loads` method in `llm_analyze_diff_size` can fail if the LLM returns malformed JSON. While there's a warning log for failures, consider enhancing error handling by returning some fallback values or raising exceptions to signal to the calling process that a review could not be completed.
- There is minimal validation applied to user inputs in the `generate_*_prompt` functions. If incorrect or unexpected data is used when generating prompts, it could lead to poor responses from the AI. Implementing validation on parameters can safeguard against misuse.

#### 5. **Code quality** (readability, naming, Django/Python idioms)
The code is fairly readable and follows Pythonic conventions. Noteworthy points:
- Documentation is good, with clear descriptions of what each function does.
- The naming convention of the prompt generation functions is clear and adheres to standard Python naming conventions.
- The inclusion of a docstring at the beginning of `llm/prompts.py` that outlines the purpose of the file is a good practice for maintainability.

Nevertheless, there could be improvements:
- The prompts themselves, although functionally sound, could include more explicit type hints or describe parameters in separate docstrings to clarify expected input for maintainers.
- Utilizing type annotations in the return types of the prompt functions can provide better clarity upfront.

#### 6. **Migration notes** — what else needs updating?
No migration tasks are required since the modified/added functions maintain backward compatibility with existing function signatures. It's recommended to update any related documentation or comments that may reference the previous inline prompts in the affected functions.

#### 7. **Verdict**: ✅ Approve
The changes presented in this PR are well-structured and enhance the modularity of the code without any incompatibility issues introduced to the API. The potential risk has been well-managed through context centralization, allowing future modifications to be less error-prone. I recommend proceeding with merging this PR after considering the minor suggestions for error handling and documentation enhancement.
