AI Agents vs Pipelines Explained
Understand the difference between AI agents and AI pipelines, when to choose each, and how to design systems that combine both for reliability and flexibility.
·5 min read · #ai#agents#pipelines
#agents
7 posts · page 1 of 1
Function Calling with LLMs: Production Patterns
How function calling really works under the hood, the schema design that survives contact with users, and the failure modes to plan for.
·5 min read · #ai#llm#function-calling
LLM Function Schema Best Practices
How to design tool schemas that LLMs actually call correctly, with naming, description, and parameter patterns that survive real users and adversarial inputs.
·4 min read · #llm#function-calling#tool-use
LLM Tool Calling and Agents Overview
Understand how tool calling lets LLMs invoke functions, why agents loop over tools, and how to design reliable tool schemas.
·5 min read · #llm#agents#tool-calling
Prompt Engineering: The ReAct Pattern
Learn the ReAct pattern, a prompting technique that combines reasoning and action to build effective tool-using LLM agents.
·4 min read · #prompt-engineering#react#agents
Prompt Engineering with Tools and Functions
How to write prompts and tool definitions that make function calling reliable. Covers schemas, descriptions, examples, error handling, and patterns for multi-tool agents.
·4 min read · #prompt-engineering#tool-use#function-calling
AI Agents and Tool Use Patterns
Practical patterns for building AI agents that use tools well: tool definitions, loops, planning, parallel calls, error handling, and how to keep agents from going off the rails.
·5 min read · #ai#agents#tool-use