AI Adoption Challenges - 95% of enterprise AI projects fail to reach production [1] Key Differentiators - The remaining 5% of successful AI projects do things differently [1]

Agent Development - Building effective Agents requires structured engineering rather than relying on simple prompting [1] - Parlant provides a framework for constructing Agents that behave precisely as instructed [1] Resource - GitHub repository available for Parlant, encouraging users to star the project [1]

95% of enterprise AI projects don't reach production.MIT’s 2025 report revealed it.Here's what the remaining 5% do differently: ...

7 LLM generation parameters, explained visually: https://t.co/UN0FRu5hytAvi Chawla (@_avichawla):7 LLM generation parameters, clearly explained (with visuals): ...

That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):7 LLM generation parameters, clearly explained (with visuals): ...

Those were the 7 most important LLM generation parameters.Here's the visual again for your reference 👇 https://t.co/Kd6k501y1t ...

7 LLM generation parameters, clearly explained (with visuals): ...

General Information - The content is a wrap-up and call to action to reshare with the network [1] - The author shares tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) daily [1] Author's Focus - Avi Chawla focuses on explaining function calling & MCP (likely referring to a specific concept within LLMs) for LLMs with visuals [1]

Core Technologies - Tool Calling enables Large Language Models (LLMs) to determine appropriate actions [1] - MCP (Model Control Plane) infrastructure ensures tool reliability, discoverability, and executability [1] - Tool Calling requests can be routed through the MCP [1]

Function calling & MCP for LLMs, clearly explained (with visuals): ...