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Overview

In this lesson, learners will explore how to structure and assemble LLM-based applications. You will learn how to translate user problems into prompts, manage context, design multi-step workflows, and evaluate outputs within a practical application framework.

Concept Explanation

1. The Anatomy of an LLM Application

• User Input → LLM → Output Transformation
  • User Input: What the end-user provides (text, question, image, etc.).
  • Prompt Engineering: Converts the user input into an LLM-friendly format.
  • LLM Output: Completion generated by the model.
  • Post-processing: Transform LLM output into actionable results for the user.
• Key Idea: An LLM application is a loop—you provide input, guide the LLM, and transform the output back to user context.

2. Converting User Problems to Model Domain

• Identify task objectives (e.g., summarization, classification, recommendation).
• Determine input requirements (e.g., structured vs. free text).
• Map user expectations to prompt format and LLM instructions.

Example:

• User Problem: “Find top risks in a financial report.”
• Model Task: Summarize key risks.
• Prompt: “You are a financial analyst. Extract the top 5 risks mentioned in this report.”

3. Context Management

• Include relevant static context (instructions, roles) and dynamic context (user-specific data).
• Techniques:
  • Elastic context: Adjust input dynamically to fit prompt size.
  • Context snippets: Include only the most relevant information for the task.
  • RAG (Retrieval-Augmented Generation): Pull supporting information from a database or knowledge base.

4. Multi-Step Workflows

• Complex applications often require multiple LLM calls:
  1. Step 1: Generate outline or analysis.
  2. Step 2: Reason over results (CoT or self-consistency).
  3. Step 3: Format output for user consumption.
• Example: AI assistant that summarizes a report, categorizes risks, and generates recommendations.

5. Evaluation & Iteration in Applications

• Evaluate at both prompt level and workflow level.
• Ensure each step produces reliable outputs before passing to the next step.
• Iteratively refine prompts, context handling, and workflow structure.

Practical Examples / Workflows

1. Customer Support Agent

Step 1: User question → LLM summarizes issue.
Step 2: LLM identifies category (billing, technical, feedback).
Step 3: LLM generates suggested response.
Step 4: Human review → send response.

2. Research Summary Application

Step 1: Pull relevant papers using RAG.
Step 2: LLM summarizes each paper.
Step 3: LLM combines summaries into a report with key insights.
Step 4: Evaluate summary quality and refine prompts iteratively.

Hands-on Project / Exercise

Task: Build a multi-step LLM workflow for a small application.

Steps:

1. Choose a simple problem (e.g., summarizing product reviews).
2. Define input, output, and LLM tasks for each step.
3. Implement prompt templates for each step, including role/context.
4. Test the workflow end-to-end.
5. Refine prompts and context based on output quality.

Goal: Deliver a reliable, structured LLM application that converts user input into actionable output.

Tools & Techniques

• LangChain or LlamaIndex: For chaining LLM calls and managing context.
• RAG (Retrieval-Augmented Generation): Integrate external knowledge bases.
• Prompt templates & dynamic snippets: Ensure efficient context use.
• Evaluation metrics: Monitor outputs at each workflow stage.

Audience Relevance

• Developers: Learn to design real-world LLM-powered applications.
• Students & Researchers: Understand workflow design and context management.
• Business Users: Automate multi-step processes, reporting, and analytics.

Summary & Key Takeaways

• LLM applications are structured workflows, not isolated prompts.
• Context management (static and dynamic) is critical for accuracy.
• Multi-step applications benefit from step-wise evaluation and prompt refinement.
• Tools like LangChain, RAG, and prompt templates enable scalable and reliable LLM apps.
• Mastering workflow design bridges fundamentals with real-world AI applications.