AI Model Training for Software Docs
Budget: ₹600 – ₹1,500 INR
I have roughly 1,000 pages of internal, software-development–focused technical documentation that I need turned into a smart, scenario-aware assistant. Once the material is ingested and the model is fine-tuned, I want to be able to ask it anything from “why does this microservice time out under load?” to “suggest a high-level redesign for our auth layer,” and receive clear, context-grounded answers. It must comfortably cover bug-fixing, troubleshooting, and software-design/architecture questions, and whenever I say “create a task,” it should generate an actionable ticket that follows my instruction style.
I’m open to whichever tooling—OpenAI fine-tuning, LangChain, RAG pipelines, vector databases like Pinecone or Weaviate—best balances quality and cost. What matters most is that the knowledge stays private, responses reflect our documentation accurately, and performance remains strong even as I add more material later.
Deliverables (acceptance criteria):
• Curated dataset: the 1,000 pages cleaned, chunked, and ready for ingestion
• Trained or retrieval-augmented model that answers the two selected domains: bug-fixing/troubleshooting and software design/architecture, with >90 % accuracy in a blind test set we’ll provide
• “Create task” function that outputs a ticket in the format: Title, Description, Acceptance Criteria, Priority
• Deployment guide and hand-off session so my team can extend the knowledge base on its own
If this sounds straightforward to you and you have proven experience training LLMs on private corpora, let’s talk and get started right away.
I’m open to whichever tooling—OpenAI fine-tuning, LangChain, RAG pipelines, vector databases like Pinecone or Weaviate—best balances quality and cost. What matters most is that the knowledge stays private, responses reflect our documentation accurately, and performance remains strong even as I add more material later.
Deliverables (acceptance criteria):
• Curated dataset: the 1,000 pages cleaned, chunked, and ready for ingestion
• Trained or retrieval-augmented model that answers the two selected domains: bug-fixing/troubleshooting and software design/architecture, with >90 % accuracy in a blind test set we’ll provide
• “Create task” function that outputs a ticket in the format: Title, Description, Acceptance Criteria, Priority
• Deployment guide and hand-off session so my team can extend the knowledge base on its own
If this sounds straightforward to you and you have proven experience training LLMs on private corpora, let’s talk and get started right away.