Optimizing Kimi API calls for automated large-scale codebase documentation updates

Kimi's long context window is a beast for codebase documentation, but if you just dump files into the prompt, you'll hit rate limits or get "lazy" summaries that skip critical logic. I've spent the last month automating the update of our internal API docs using Kimi, and the secret isn't in the prompt, but in how you structure the context feeding.

The biggest mistake is sending the entire codebase every time. Even with huge context windows, the "lost in the middle" phenomenon is real. I shifted to a "Dependency-Aware Chunking" strategy. Instead of raw files, I use a script to parse the AST (Abstract Syntax Tree) to identify which functions actually changed and which other files they reference.

Here is the basic logic I used for the pre-processing script to filter context:

import ast

def get_referenced_symbols(file_path):
    with open(file_path, "r") as f:
        tree = ast.parse(f.read())
    
    references = []
    for node in ast.walk(tree):
        if isinstance(node, ast.Name) and isinstance(node.ctx, ast.Load):
            references.append(node.id)
    return references

# Only send the changed file + the specific signatures of referenced symbols
# rather than the full source of every dependency.

When calling the Kimi API, I found that the temperature needs to be dialed way down to 0.2 for documentation. Anything higher and it starts adding "fluff" adjectives like "powerful" or "seamlessly" which just clutter technical docs.

For the prompt, stop asking it to "update the documentation." That's too vague. I use a "Diff-to-Doc" pattern. I feed it the Git diff of the change and the existing documentation block, then tell it to act as a technical writer who hates redundancy.

System Prompt: You are a senior technical writer. 
Task: Update the existing documentation based on the provided git diff.
Rule 1: Only change sections affected by the code change.
Rule 2: Maintain the existing Markdown style and tone.
Rule 3: If the logic hasn't changed despite the code change (e.g., refactoring), do not update the doc.
Output: Return only the updated Markdown section.

A major gotcha with large-scale updates is the API's output length. If you're updating a 2,000-line README, Kimi might truncate the response. To fix this, I implemented a "Sectional Update" loop. I split the documentation into logical blocks (headers), identify which block needs the update via the diff, and only send that specific block for editing.

Productivity Gains & Configs:

Context Pruning: By sending only the AST-referenced signatures instead of full files, I reduced token usage by 60% without losing accuracy.

Batching: I use a concurrent queue to handle API calls, but I've capped it at 5 concurrent requests. Pushing more than that on a standard Kimi tier often leads to intermittent 503 errors.

Validation: I added a post-processing step that runs a simple regex check to ensure the AI didn't accidentally delete the existing ## Table of Contents or other structural elements.

The result is a CI/CD pipeline that automatically flags documentation gaps when a PR is merged, and in 80% of cases, the suggested updates are a one-click approval.