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Overview

This lesson teaches learners how to design interactive AI systems capable of multi-step reasoning, memory of context, and human-like conversation. You will learn the principles behind chat workflows, reasoning chains, and task-oriented conversational agents.

Concept Explanation

1. Multi-Step Reasoning

• LLMs can perform complex tasks by reasoning step by step rather than giving one-shot answers.
• Techniques:
  • Chain-of-Thought (CoT): Guide the model to reason sequentially.
  • Tree-of-Thought (ToT): Explore multiple solution paths simultaneously.
  • Self-consistency: Generate multiple reasoning chains and select the most consistent answer.
• Benefits:
  • Reduces errors in multi-step calculations, logic, or planning.
  • Produces structured outputs that are easier to validate.

2. Conversational Agents

• Conversational agents simulate interactive dialogue and maintain context across multiple turns.
• Components:
  1. User Input: Question, command, or request.
  2. Context Memory: Keeps track of previous conversation or relevant data.
  3. Reasoning & Response Generation: LLM generates replies using prompts and context.
  4. Post-processing: Formats output, triggers actions, or queries external data.
• Key concept: A conversational agent is stateful, unlike single-turn LLM queries.

3. Context for Task-Based Interactions

• Agents need to maintain:
  • Short-term memory: Current conversation context.
  • Long-term memory: Persistent user information or preferences.
• Strategies:
  • Chunk and summarize conversation history.
  • Use embeddings for semantic search across conversation history.
  • Limit context to token capacity while retaining essential information.

4. Tool-Augmented Agents

• LLMs can interact with external tools or APIs (ReAct framework):
  • Search engines, databases, calculators, or internal company systems.
• This allows agents to reason, act, and retrieve data dynamically.

5. Building a Conversational Agent Workflow

• Step 1: Define task and role of the agent.
• Step 2: Set up input processing and context handling.
• Step 3: Integrate reasoning techniques (CoT, self-consistency).
• Step 4: Implement external tool calls if needed.
• Step 5: Post-process outputs for clarity, format, and action triggers.
• Step 6: Iteratively test and refine conversation flows.

Practical Examples / Prompts

1. Multi-Step Reasoning

Prompt: "Plan a 3-day itinerary in Paris, considering budget, weather, and sightseeing preferences. Explain each step of the planning."

2. Conversational Agent

System Message: "You are a travel assistant."
User Message: "I want to visit Paris in June."
Agent Action: Retrieve flights, hotels, and attractions.
Agent Response: "Here’s a 3-day itinerary including flights and hotel suggestions."

3. Tool-Augmented Agent

Prompt: "You are an AI assistant. Retrieve today’s weather for Paris, then suggest appropriate sightseeing activities."

Hands-on Project / Exercise

Task: Build a mini conversational agent for customer support.

Steps:

1. Define scope (e.g., order tracking, FAQs, returns).
2. Create prompts with system instructions and role definitions.
3. Implement context memory for multi-turn interactions.
4. Optionally, integrate tools like product databases or shipment APIs.
5. Test conversations, evaluate output accuracy, and refine prompts iteratively.

Goal: Create an agent that handles multi-turn queries reliably and provides actionable responses.

Tools & Techniques

• Frameworks: LangChain, LlamaIndex for conversation management.
• Memory Management: Store short-term and long-term context.
• Reasoning Techniques: CoT, self-consistency, ToT.
• Tool Integration: ReAct framework to interact with external APIs or databases.
• Evaluation: Test multi-turn interactions for consistency and correctness.

Audience Relevance

• Developers: Build interactive AI assistants and task-oriented agents.
• Students & Researchers: Learn multi-step reasoning and context management.
• Business Users: Automate customer support, internal help desks, or interactive reporting.

Summary & Key Takeaways

• Multi-step reasoning ensures structured, accurate outputs.
• Conversational agents are stateful and task-oriented, retaining context across turns.
• Tool integration allows agents to reason and act on real-world data.
• Iterative testing and context management are crucial for robust multi-turn applications.
• Mastery of these techniques bridges fundamentals with real-world AI application design.