Better Than ChatGPT

Developers often reach for a general LLM (hello, ChatGPT) when they want to understand a repo. That makes sense — large language models are great at summarizing text. But a lot changes when the unit of work is a codebase: files, imports, tests, README gaps, and hidden conventions.

Below I compare a real-world example (DeepCode → PDF→code pipeline) and show why a repo-aware tool like ExplainGitHub gives faster, more precise, and action-ready answers than a general ChatGPT-style approach.

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TL;DR

• ChatGPT gave a solid, high‑level guess in ~2.5 minutes — helpful for concepts.
• ExplainGitHub returned a file‑level trace in ~3 seconds — helpful for action.
• For onboarding, debugging, and tracing flows, repo‑aware answers with citations win.

Try it yourself: open any GitHub repo and replace github.com with explaingithub.com.

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The two approaches — different problems, different strengths

ChatGPT-style (general LLM)

• Strength: broad reasoning, conceptual synthesis, natural-language summaries.
• How it works here: given a repo link, it infers likely components and an inferred workflow (agents, pipeline stages, probable files) from README and public signals.
• Best when: you want quick conceptual understanding, high-level ideas, or brainstorming.

ExplainGitHub (repo-aware)

• Strength: direct, file-level evidence, fast repo indexing and retrieval, structured outputs (file paths, function names, components, diagrams).
• How it works here: the repository is parsed, indexed (embeddings/retrieval), and the assistant answers using the actual code and files as context — returning exact filenames, functions, and call traces.
• Best when: you need precise, actionable steps — “Where is this implemented?” or “Which file to edit?”

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A concrete comparison (DeepCode example)

ChatGPT response (summary):

• Output: a high-level inferred pipeline (file-downloader → segmentation → indexer → code-implementation).
• Nature: conceptual; recommended files and exact code paths not enumerated.
• Time: ~2 minutes 30 seconds.

ExplainGitHub response (repo‑aware):

• Output: explicit file-level trace:
  • ui/components.py → file_input_component handles uploads
  • tools/pdf_converter.py → PDFConverter used when needed
  • ui/handlers.py → handle_start_processing_button, process_input_async, handle_processing_workflow
  • workflows/agent_orchestration_engine.py → execute_multi_agent_research_pipeline used for paper->code pipeline
  • ui/layout.py → results_display_component shows the output
• Nature: actionable — you can click the files (in ExplainGitHub UI), inspect the functions, and verify.
• Time: ~3 seconds.

Why the difference? ExplainGitHub is reading your repo directly, indexing code and docstrings and mapping function names to usage. ChatGPT is reasoning from general knowledge and README cues.

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Why file-aware answers matter (three developer examples)

1. Onboarding a new teammate

   • ChatGPT: “This repo probably has an auth module and a service layer.” (good to know)
   • ExplainGitHub: “Start at src/auth/login.py → AuthService.authenticate() → db/session.py::create_session().” (exact path for hands-on work)

2. Fixing a bug quickly

   • ChatGPT: “Likely causes: misconfigured DB, missing validation.”
   • ExplainGitHub: “The 500 error originates in api/image_upload.py:handle_upload() — check utils/image_utils.py:resize_image() for missing try/catch.” (gives the line of attack)

3. Preparing for a feature

   • ChatGPT: “You’ll need to update auth and session handling.”
   • ExplainGitHub: “Change auth/config.py and add middleware in app/middleware/security.py. See tests in tests/test_auth.py to update.” (gives checklist and tests to run)

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How ExplainGitHub achieves this (high level)

• Repo ingestion & parsing — files, folders, README, docs, and code comments are parsed and tokenized.
• Indexing / retrieval — embeddings or other indices allow fast retrieval of the file chunks relevant to a question.
• Context assembly — when you ask, the system pulls the most relevant file snippets and uses them as the immediate context for the LLM.
• File citation — answers include exact file paths and function references so you can go look/verify.
• Conversation context — Saved history shows structure so you can mentally map the flow before diving into files.

(If you want a technical whitepaper of our ingestion/embedding approach, we can publish a separate piece.)

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Examples: Good prompts vs weak prompts

Weak prompt (ChatGPT often sees this):

“Tell me everything about this repo.”

Result: generic overview — not helpful for action.

Good prompt (ExplainGitHub style):

“Show the HTTP request → controller → service → DB flow for createOrder() and list the exact files and functions involved.”

Result: precise trace with filenames and functions — immediately actionable.

Best practice: be specific about the intent (trace, debug, add feature) and the unit (function name, endpoint, module).

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Practical advice for using ExplainGitHub effectively

1. Start with a one-line intent: “I want to add X” or “Trace Y flow.”
2. Ask for file citations: “Cite file paths in answers.”
3. Narrow the scope: “Only look in src/services/ and src/controllers/.”
4. Ask follow-ups: “Show unit tests related to that function.”
5. Review recent history to reorient quickly, then ask targeted chat questions.

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Limitations & when to use ChatGPT instead

• ExplainGitHub shines when you need verifiable, repo-specific answers.
• If you want broad brainstorming, high-level strategy, or cross-repo theoretical design, a generic LLM is still great.
• Large monorepos or repos with massive binary content may require longer ingestion times or chunking; ExplainGitHub handles this with chunking and retrieval strategies, but very large analyses can take longer.

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Conclusion — use the right tool for the right job

• For quick ideas and conceptual overviews, general LLMs are useful.
• For practical code exploration, debugging, onboarding, and modification, you want a repo-aware assistant like ExplainGitHub that answers with verifiable file paths and function-level traces — and does it fast.

If you want to try it now, open any repo and replace github.com with explaingithub.com — then ask:

“Trace the main request → controller → DB flow for the upload endpoint and show the files.”

You’ll see the difference in seconds.