AWS CodeWhisperer Enterprise Customization (Inception to GA)

Research & Engineering Scope

At Amazon Web Services (AWS) AI Labs, I co-led applied research for CodeWhisperer (now Amazon Q Developer) enterprise code customization from concept to General Availability. The challenge was enabling enterprise customers to adapt a shared base code LLM to their private repositories without leaking data across tenants or blowing up multi-tenant GPU memory budgets.

Key Contributions & Milestones

• Multi-Tenant Adapter Architecture: Architected the production recipe combining real-time repository RAG with LoRAX-style parameter-efficient fine-tuning (PEFT), enabling dedicated per-customer adapters running concurrently on shared base model clusters.
• Systematic Post-Training Sweeps: Directed extensive empirical benchmarks comparing full fine-tuning against LoRA, DoRA, and AdaLoRA across ranks, alphas, target projection modules, and learning rate warmups, setting AWS’s production customization training standards.
• Inference-Time Symbol Hallucination Detection: Identified and solved critical hallucination modes in code generation by building an AST-based symbol validator that blocked invalid functions, methods, and classes at inference time before code was rendered to developers.
• Outcome-Grounded Evaluation: Replaced synthetic benchmark metrics (like BLEU or raw pass@k) with developer-grounded quality indicators: real-world execution pass rate, symbol hallucination incidence, and developer acceptance rate.