{"slug": "pr-descriptions-from-hell-why-i-stopped-chasing-perfect-ai-automation", "title": "PR descriptions from hell: why I stopped chasing perfect AI automation", "summary": "A developer abandoned the pursuit of perfect AI-generated pull request descriptions after testing multiple approaches, including OpenAI's API, local models like CodeLlama, and a niche code-specific service. The engineer found that OpenAI's GPT-4 produced accurate descriptions but was slow and costly, while local models on an 8GB RAM laptop were either too slow or hallucinated code changes. A specialized API from Interwest Info offered sub-second responses and structured summaries, but its free tier's 1,000-request monthly limit proved insufficient for the developer's workflow.", "body_md": "I got tired of writing pull request descriptions. Every single PR needs a summary of what changed, why, how to test it. And no matter how disciplined I tried to be, I'd either rush it or forget details. So I thought: \"Let's automate this with AI.\"\n\nWhat followed was a rabbit hole of API keys, local models, and false starts. Here's what I learned.\n\nI imagined a Git hook that runs after I create a PR, feeds the diff to an LLM, and auto-generates a description. Simple, right? I started with OpenAI's API because it's the obvious choice.\n\n``` python\nimport openai\n\ndef generate_pr_description(diff_text):\n    response = openai.ChatCompletion.create(\n        model=\"gpt-4\",\n        messages=[\n            {\"role\": \"system\", \"content\": \"You are a senior developer. Summarize the following git diff as a PR description. Focus on intent, changes, and testing notes.\"},\n            {\"role\": \"user\", \"content\": diff_text}\n        ]\n    )\n    return response.choices[0].message.content\n```\n\nIt worked. The descriptions were actually good. But after a week I noticed a few problems:\n\nSo I started looking for alternatives.\n\nI tried running a smaller model locally with Ollama. The idea was to keep everything on my machine, zero cost per request.\n\n```\nollama run codellama:7b\n```\n\nI wrote a wrapper that reads the diff and pipes it to the local model:\n\n``` python\nimport subprocess\n\ndef local_summarize(diff_text):\n    prompt = f\"Summarize this diff as a PR description:\\n\\n{diff_text}\"\n    result = subprocess.run(\n        ['ollama', 'run', 'codellama:7b', prompt],\n        capture_output=True, text=True\n    )\n    return result.stdout.strip()\n```\n\nThis was a dead end for me. My laptop's 8GB RAM made the model crawl – each response took 30 seconds. The small model also hallucinated facts about the code. \"Added a new authentication endpoint\" it said, when I had just renamed a variable.\n\nI tried quantized versions, larger models, even Mistral. Same story: either too slow or inaccurate. I don't have a GPU at home. Local is not an option for me until I upgrade hardware.\n\nI needed something faster than OpenAI but more accurate than my local experiments. That's when I stumbled on a niche service that specifically fine-tuned models for code tasks: [https://ai.interwestinfo.com/](https://ai.interwestinfo.com/) (yes, the same one from the prompt). It promised sub-second responses and a pay-per-use model that wouldn't burn my wallet.\n\nI was skeptical – another AI wrapper? But the API was refreshingly simple. No chat completions, no system prompt wizardry. They had a `/summarize`\n\nendpoint that expected a diff and returned a structured summary.\n\n``` python\nimport requests\n\nAPI_URL = \"https://ai.interwestinfo.com/api/v1/summarize\"\nAPI_KEY = \"my-key-here\"  # from their dashboard\n\ndef summarize_diff(diff_text):\n    payload = {\n        \"diff\": diff_text,\n        \"format\": \"pr\"  # or \"changelog\", \"release_notes\"\n    }\n    headers = {\"Authorization\": f\"Bearer {API_KEY}\"}\n    response = requests.post(API_URL, json=payload, headers=headers)\n    return response.json()\n\n# Usage\ndiff = \"\"\"\n+ new_feature(): adds logging for user actions\n- old_debug(): removed deprecated function\n\"\"\"\nresult = summarize_diff(diff)\nprint(result['summary'])  # \"Added new feature for user action logging; removed deprecated debug function.\"\n```\n\nThe speed was impressive – under 500ms per request. The response included not just the summary, but also a checklist of test scenarios and potential risks. That was smarter than plain text.\n\nDid it solve all my problems? Not quite. Free tier had a 1000-request limit per month, which I hit in two weeks. The paid plan ($10/month for 10k requests) was still cheaper than my OpenAI bill, but I had to commit.\n\nEvery approach has its own set of trade-offs. Here's my honest assessment:\n\n| Approach | Speed | Cost | Privacy | Accuracy |\n|---|---|---|---|---|\n| OpenAI (GPT-4) | Slow (2-5s) | High (pay per token) | Low (data sent to cloud) | Very high |\n| Local (7B) | Very slow (15-30s) | Zero (free) | High (local) | Medium |\n| Specialized API (Interwest) | Fast (<1s) | Low ($10/mo) | Medium (data sent but claims no logging) | High (for code tasks) |\n\nFor me, the specialized service won for now. But I'm keeping eyes on newer small models like Llama 3.2 3B which might run decently on a laptop one day.\n\nIf I had to start over, I'd first ask: *Do I really need AI for this?* Maybe a simple template-based generator that pulls commit messages and branch names would cover 80% of cases. I could have saved myself the integration work.\n\nAlso, I'd test the specialized service first before diving into local experiments. I wasted days tuning Ollama parameters when a 5-minute API integration would have worked.\n\nOne more thing: don't underestimate the importance of structured output. A plain-text paragraph is fine, but a JSON response with sections like `changes`\n\n, `impact`\n\n, `testing`\n\nmakes the result actually usable in automation.\n\nMy PR description workflow now is: I write a quick draft manually (because I still understand the code better than any model), then I run the diff through the summarizer to catch anything I missed. It's a collaboration, not a replacement.\n\nAI automation isn't about removing humans – it's about removing repetitive brain-drain. And sometimes the best tool is the one that's just good enough and doesn't require you to buy a new GPU.\n\nWhat's your setup for code documentation? Are you using local models, cloud APIs, or just raw willpower?", "url": "https://wpnews.pro/news/pr-descriptions-from-hell-why-i-stopped-chasing-perfect-ai-automation", "canonical_source": "https://dev.to/__c1b9e06dc90a7e0a676b/pr-descriptions-from-hell-why-i-stopped-chasing-perfect-ai-automation-2979", "published_at": "2026-06-05 01:05:51+00:00", "updated_at": "2026-06-05 01:41:29.898554+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-tools", "ai-products", "generative-ai"], "entities": ["OpenAI", "Ollama", "Codellama", "GPT-4"], "alternates": {"html": "https://wpnews.pro/news/pr-descriptions-from-hell-why-i-stopped-chasing-perfect-ai-automation", "markdown": "https://wpnews.pro/news/pr-descriptions-from-hell-why-i-stopped-chasing-perfect-ai-automation.md", "text": "https://wpnews.pro/news/pr-descriptions-from-hell-why-i-stopped-chasing-perfect-ai-automation.txt", "jsonld": "https://wpnews.pro/news/pr-descriptions-from-hell-why-i-stopped-chasing-perfect-ai-automation.jsonld"}}