Project: AI-Powered Knowledge Base for Employees
Employees struggled to find relevant documents, insights, and technical references.
Keyword search systems returned irrelevant results and lacked context.
Decision-making was slow due to manual browsing and inconsistent documentation.
Solution Overview
Built an internal LLM-powered Knowledge Assistant integrated with Slack.
Used RAG for contextual search across wikis, product manuals, Jira tickets & Slack threads.
Added real-time document embedding and feedback-driven learning loops.
Steps in Developing the Solution
1. Model Selection & Fine-Tuning
Evaluated models: GPT-4, Claude 2, LLaMA 3, and Mistral Large.
GPT-4 chosen for deeper reasoning, strong context handling, and safer outputs.
Fine-tuned using company documentation + support transcripts.
Applied RLHF to refine tone & accuracy for internal use cases.
2. Performance Optimization
Integrated vector embeddings for fast semantic retrieval.
Optimized model prompting through a hybrid RAG + System Instructions approach.
Cached frequently asked queries to cut latency by 40%.
3. Prompt Engineering & RAG
Implemented multi-step prompting: search → retrieve → filter → generate.
Reduced hallucinations by grounding every response in retrieved context.
Introduced "source citations" to increase trust.
4. User Testing
Conducted 3-round testing with engineers, sales teams, and PMs.
Created edge-case documents to test content contradictions, outdated material, and missing data.
Logged all failure cases → added to fine-tuning dataset.
5. Deployment
Integrated directly into Slack using a low-friction command interface.
Added weekly automated feedback flows to capture user satisfaction and error reports.
Results & Business Impact
55% reduction in time spent searching for internal information.
95% accurate domain-grounded responses with RAG.
High adoption: 72% active weekly users within month 1.
Improved decision-making speed and reduced onboarding time for new employees.