Advance LLM patterns

In this section, we explore common patterns in LLM applications that enhance the model’s capabilities without relying on agent-based control flows. These non-agentic patterns focus on leveraging the strengths of LLMs in structured and predictable ways.

4.1 Chain of Thought

  • Fosters logical reasoning by prompting the LLM to break down tasks step-by-step.

  • Improves coherence and context retention in multi-step problem-solving scenarios.

  • Enhances the model’s ability to generate structured, sequential responses.

4.2 Tools Usage from LLM Perspective

  • Involves the LLM invoking external tools (e.g., APIs, search engines) to gather information.

  • Supports tasks requiring real-time data retrieval, complex computations, or system interactions.

  • Extends the LLM’s functionality beyond its static training data.

4.3 ReAct Prompting

  • Combines reasoning and action steps, guiding the LLM to think and act iteratively.

  • Uses a Thought → Action → Observation loop for dynamic task execution.

  • Enhances decision-making by integrating external information into the reasoning process.

4.4 Retrieval Augmented Generation (RAG)

  • Merges information retrieval with response generation, grounding the LLM’s answers in real-time data.

  • Retrieves relevant documents or knowledge snippets to enhance response accuracy.

  • Ideal for applications needing up-to-date or domain-specific information, reducing hallucination risks.