Practical Applications
RAG, agents, tool use, and prompt engineering.
Prompt Engineering
Prompt engineering is the discipline of crafting inputs to large language models that reliably elicit the desired outputs, bridging the gap between what a model can do and what you actually need it to do.
Structured Output & JSON Mode
Structured output techniques constrain LLM generation to produce reliably parseable formats like JSON, XML, or YAML, transforming probabilistic text generation into deterministic, schema-conformant outputs essential for software integration.
Function Calling & Tool Use
Function calling enables LLMs to interact with the outside world by generating structured requests (typically JSON) that an application layer executes and feeds back, transforming language models from text generators into general-purpose reasoning engines that can take real actions.
Retrieval-Augmented Generation (RAG)
RAG grounds LLM responses in external knowledge by retrieving relevant documents at query time and injecting them into the prompt, dramatically reducing hallucination and enabling models to answer questions about data they were never trained on.
Chunking Strategies for RAG
Chunking is the process of splitting documents into smaller pieces for embedding and retrieval, and the choice of chunking strategy directly determines whether a RAG system retrieves useful context or useless fragments.
Embedding Models & Vector Databases
Embedding models transform text into numerical vectors that capture semantic meaning, and vector databases store and search those vectors at scale, together forming the retrieval backbone of modern LLM applications.
AI Agents
AI agents are systems where LLMs operate in autonomous loops – reasoning about a task, taking actions through tools, observing results, and iterating until the goal is achieved – moving beyond single-response generation into multi-step problem solving.
ReAct Pattern (Reasoning + Acting)
ReAct interleaves chain-of-thought reasoning with tool-calling actions in a unified Thought-Action-Observation loop, grounding LLM reasoning in real-world feedback.
Self-Reflection and Reflexion
Self-reflection enables LLM agents to evaluate, critique, and iteratively improve their own outputs across trials by converting feedback into natural language memory.
Memory Systems for LLM Agents
Memory systems extend LLM agents beyond the context window by providing structured mechanisms for storing, retrieving, and managing information across interactions and sessions.
Multi-Agent Systems
Multiple LLM-powered agents collaborate through defined roles, tools, and communication protocols to solve problems that exceed the capability of any single agent.
Model Context Protocol (MCP)
MCP is an open standard that provides a universal interface for connecting LLM applications to external data sources, tools, and services – replacing fragile, custom integrations with a single, composable protocol.