Uploaded April 2025 | Updated September 2026, 3 weeks ago
In this video, we reveal how to optimize LLMs for tool use without wrecking latency:
Build fine-tuning datasets mirroring tools like AgentFlan—convert internal APIs (e.g., Torch uploads) into synthetic prompts/responses.
Use AST matching to verify tool calls (is this code snippet valid for your library?) and block hallucinated functions.
Master confidence-based routing: Should the LLM answer “5+3” itself or call a calculator?
Learn how to make your agent smarter, not slower!
#AIAgents #LLMOptimization #FineTuning #AITools #MachineLearning #AILatency #AIEngineering #GuardrailsAI #PromptEngineering
Where else to find us:
linkedin.com/in/amirfzpr
aisc.substack.com
youtube.com/@ai-science
https://lu.ma/aisc-llm-school
maven.com/aggregate-intellect
In this video, we reveal how to optimize LLMs for tool use without wrecking latency:
Build fine-tuning datasets mirroring tools like AgentFlan—convert internal APIs (e.g., Torch uploads) into synthetic prompts/responses.
Use AST matching to verify tool calls (is this code snippet valid for your library?) and block hallucinated functions.
Master confidence-based routing: Should the LLM answer “5+3” itself or call a calculator?
Learn how to make your agent smarter, not slower!
#AIAgents #LLMOptimization #FineTuning #AITools #MachineLearning #AILatency #AIEngineering #GuardrailsAI #PromptEngineering
Where else to find us:
linkedin.com/in/amirfzpr
aisc.substack.com
youtube.com/@ai-science
https://lu.ma/aisc-llm-school
maven.com/aggregate-intellect
