Project 4: Conversational Product Recommender

Build a conversational recommender that asks clarifying questions and returns ranked product suggestions.

Quick Reference

Attribute Value
Difficulty Level 2: Intermediate
Time Estimate 8-12 hours
Language Python or JavaScript
Prerequisites Prompting basics, simple data storage
Key Topics conversational state, ranking, user preferences

1. Learning Objectives

By completing this project, you will:

  1. Track conversational state and preferences.
  2. Ask clarifying questions when data is missing.
  3. Rank products based on criteria.
  4. Explain why each recommendation was made.
  5. Log user feedback for improvement.

2. Theoretical Foundation

2.1 Preference Elicitation

Good recommendations depend on knowing user constraints. The agent should ask targeted questions before ranking.

3. Project Specification

3.1 What You Will Build

A chat-style recommender that guides a user to a shortlist of products with explanations.

3.2 Functional Requirements

  1. State manager for preferences.
  2. Question generator for missing data.
  3. Ranking logic with configurable weights.
  4. Explainability for each result.
  5. Feedback capture for refinement.

3.3 Non-Functional Requirements

  • Deterministic ranking for identical inputs.
  • Safe defaults when data is incomplete.
  • Session persistence for multi-turn flows.

4. Solution Architecture

4.1 Components

Component Responsibility
Dialogue Manager Track user state
Question Generator Ask for missing prefs
Ranker Score candidates
Explainer Provide rationale

5. Implementation Guide

5.1 Project Structure

LEARN_LANGCHAIN_PROJECTS/P04-product-recommender/
├── src/
│   ├── state.py
│   ├── questions.py
│   ├── ranker.py
│   ├── explain.py
│   └── chat.py

5.2 Implementation Phases

Phase 1: State + questions (3-4h)

  • Track preferences and ask for missing info.
  • Checkpoint: agent asks the right follow-up.

Phase 2: Ranking (3-4h)

  • Implement scoring and sorting.
  • Checkpoint: top-3 results change with preferences.

Phase 3: Explainability (2-4h)

  • Add rationale for each recommendation.
  • Checkpoint: explanations cite preferences.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit ranking correct order given weights
Integration dialogue follow-up questions triggered
Regression explain rationale includes prefs

6.2 Critical Test Cases

  1. Missing budget triggers a clarifying question.
  2. Ranking is stable for identical inputs.
  3. Explanation references user constraints.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Generic questions user confusion target missing fields
Unstable ranking inconsistent output sort by stable keys
Weak explanations no trust include preference matches

8. Extensions & Challenges

Beginner

  • Add small product catalog CSV.
  • Add a “why not” explanation.

Intermediate

  • Add multi-objective ranking.
  • Add user feedback loop.

Advanced

  • Add bandit-style exploration.
  • Add personalization embeddings.

9. Real-World Connections

  • E-commerce chatbots rely on preference-driven ranking.
  • Sales assistants use explainable recommendations.

10. Resources

  • Recommender systems basics
  • LangChain memory patterns
  • “AI Engineering” (product assistants)

11. Self-Assessment Checklist

  • I can track user preferences in conversation.
  • I can rank results deterministically.
  • I can explain recommendations clearly.

12. Submission / Completion Criteria

Minimum Completion:

  • Conversational flow + ranking
  • Explanations for top results

Full Completion:

  • Session persistence
  • Feedback capture

Excellence:

  • Adaptive ranking with feedback
  • Personalization features

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/LEARN_LANGCHAIN_PROJECTS.md.