Tested and evaluated LLM performance within RAG pipelines, API integrations, and MCP server environments, identifying weaknesses and guiding models toward more accurate, context-aware outputs. Created prompt engineering frameworks and templates for coding, mathematics, and various specialized domains/use cases, boosting precision and reproducibility of outputs by about 20%. Measured and analyzed the progress of LLMs in answering/solving complicated user queries and evaluated their reasoning using multiple thorough axes. Worked with clients such as Anthropic and xAI to gauge model abilities and analyze strengths and weaknesses through robust criteria. Conducted cross-model benchmarking (Claude Sonnet 4.0, GPT-5, Gemini, Grok, etc.), reducing reasoning errors by upwards of 28% and improving reliability. Designed hooks and steering strategies for AI IDEs (Cursor, Kiro), including use of JSON and MDC, which improved code-generation quality and nearly halved the necessity for developer inte