LangChain Mastery

Course Overview

Intermediate

Free Course

For Python developers and AI engineers preparing for interviews on building LLM applications with LangChain. You will be able to answer questions on LCEL chains, models and prompts, agents and tools, memory, retrieval, debugging and production practice, and explain how each legacy API maps to its current LangChain 1.x equivalent.

Instructor: MantraMindAI

Sections: 7

Course Content

Section 1: LangChain Basics

Prepares you to explain what LangChain is, how its packages and core pieces fit together in the 1.x release, and how to configure models, prompts, providers, validation and callbacks in working code.

1. What is LangChain, and how is it used in NLP applications?

2. What are the core components of LangChain?

3. How do you install LangChain and its dependencies?

4. How do you configure an LLM in LangChain for text generation?

5. What is a PromptTemplate in LangChain, and how is it used?

6. How do you save and load a LangChain chain for reuse?

7. Write a function to create a simple LangChain LLMChain for text summarization.

8. How do you use LangChain to integrate with external APIs like OpenAI?

9. How do you handle API rate limits in LangChain?

10. Write a function to generate text completions with LangChain.

11. How do you use LangChain to switch between different LLM providers?

12. Implement a custom LangChain LLM wrapper for a local model.

13. How do you implement a custom prompt template with dynamic inputs?

14. Write a function to validate LangChain LLM outputs.

15. How do you use LangChain to handle multi-lingual NLP tasks?

16. Implement a LangChain callback for logging execution time.

Section 2: Chains

Prepares you to build, explain and debug LCEL chains, from simple sequences and output parsing to batching, error handling, parallel branches and routing, and to map legacy chain classes to their current equivalents.

1. What is a chain in LangChain, and how is it used in NLP?

2. How do you create a sequential chain in LangChain?

3. What is the difference between LLMChain and SequentialChain?

4. How do you pass inputs to a LangChain chain?

5. What is the role of output parsers in LangChain chains?

6. How do you debug a LangChain chain?

7. Write a function to create a LangChain chain for question answering.

8. How do you implement a chain with multiple prompts in LangChain?

9. Write a function to chain text generation and parsing.

10. How do you use LangChain to create a conversational chain?

11. Implement a chain to handle batch processing in LangChain.

12. How do you handle errors in LangChain chains?

13. Write a function to implement a custom LangChain chain.

14. How do you optimize LangChain chains for low-latency NLP tasks?

15. Write a function to implement a parallel chain execution in LangChain.

16. How do you implement a chain with dynamic routing in LangChain?

17. Implement a chain to handle multi-step reasoning in LangChain.

Section 3: Agents and Tools

Prepares you to explain how a LangChain agent chooses and calls tools, build one with create_agent in LangChain 1.x, and answer the follow-ups on errors, timeouts, debugging, multi-agent design and production monitoring.

1. What is a LangChain agent, and how is it used in NLP?

2. How do you create a tool in LangChain for an agent?

3. What is the ReAct framework in LangChain agents?

4. How do you use LangChain agents for web search integration?

5. What is the role of the agent executor in LangChain?

6. How do you handle tool failures in LangChain agents?

7. Write a function to create a LangChain agent with custom tools.

8. How do you implement a LangChain agent with memory?

9. Write a function to integrate a LangChain agent with a database tool.

10. Implement a LangChain agent to handle API-based tools.

11. How do you debug LangChain agent decision-making?

12. Write a function to implement a custom LangChain agent.

13. How do you optimize LangChain agents for complex NLP tasks?

14. Write a function to implement a multi-agent system in LangChain.

15. How do you implement a LangChain agent with tool prioritization?

16. Write a function to handle tool timeouts in LangChain agents.

17. How do you monitor LangChain agent performance in production?

Section 4: Memory

Prepares you to explain how a LangChain app remembers a conversation, map the legacy memory classes and message histories to today's LangGraph checkpointers and stores, and answer the follow-ups on token limits, summaries, persistence and multi-user safety.

1. What is memory in LangChain, and how is it used in NLP?

2. How do you add memory to a LangChain chain?

3. What is ConversationBufferMemory in LangChain?

4. How do you retrieve memory from a LangChain conversation?

5. What is the role of memory keys in LangChain?

6. How do you clear memory in a LangChain conversation?

7. Write a function to create a LangChain chain with summary memory.

8. How do you implement token-limited memory in LangChain?

9. Write a function to store conversation history in a database.

10. How do you use LangChain to implement entity-based memory?

11. Implement a function to merge multiple memory contexts.

12. How do you handle memory overflow in LangChain?

13. Write a function to implement a custom memory type in LangChain.

14. How do you implement memory with vector stores in LangChain?

15. How do you implement memory for multi-user conversations in LangChain?

16. How do you optimize memory for long conversations in LangChain?

Section 5: Retrieval and Vector Stores

Prepares you to explain and build retrieval-augmented generation in LangChain — embeddings, vector stores, retrievers, hybrid and filtered search, and how to measure whether retrieval works.

1. What is retrieval-augmented generation (RAG) in LangChain?

2. How do you create a vector store in LangChain?

3. What is the role of embeddings in LangChain retrieval?

4. How do you use LangChain to query a vector store?

5. What is the RetrievalQA chain in LangChain?

6. How do you save and load a vector store in LangChain?

7. Write a function to create a LangChain RAG pipeline.

8. How do you implement a custom retriever in LangChain?

9. Write a function to update a LangChain vector store with new documents.

10. How do you use LangChain to implement semantic search?

11. Implement a function to combine multiple vector stores in LangChain.

12. How do you optimize vector store retrieval in LangChain?

13. Write a function to implement a hybrid search in LangChain.

14. How do you implement a LangChain retriever with metadata filtering?

15. Write a function to implement a self-querying retriever in LangChain?

16. How do you handle large-scale vector stores in LangChain?

17. Write a function to implement a contextual compression retriever.

18. How do you evaluate retrieval performance in LangChain?

Section 6: Debugging and Error Handling

Prepares you to explain how you find and fix failures in LangChain apps and agents — tracing, callbacks, retries, fallbacks, validation and production logging.

1. How do you debug a LangChain chain that fails?

2. What is a try-except block in LangChain applications?

3. How do you validate inputs for LangChain chains?

4. What is the role of verbose mode in LangChain?

5. How do you log errors in LangChain applications?

6. Write a function to retry LangChain chain execution on failure.

7. How do you debug LangChain agent tool selection?

8. Implement a function to validate LangChain output formats.

9. How do you profile LangChain chain performance?

10. Write a function to handle memory errors in LangChain?

11. How do you debug LangChain retrieval issues?

12. Write a function to implement a custom error handler for LangChain.

13. How do you implement circuit breakers in LangChain applications?

14. Write a function to detect and handle LLM hallucination in LangChain.

15. How do you implement logging for distributed LangChain applications?

16. Write a function to handle version compatibility in LangChain.

17. How do you debug LangChain memory issues in long conversations?

Section 7: Best Practices and Optimization

Prepares you to answer how you structure, configure, cache, test and scale LangChain applications so they stay reliable and affordable in production.

1. What are best practices for structuring LangChain code?

2. How do you ensure reproducibility in LangChain applications?

3. What is caching in LangChain, and how is it used?

4. How do you handle large-scale data in LangChain applications?

5. What is the role of environment configuration in LangChain?

6. How do you document LangChain applications?

7. Write a function to optimize LangChain memory usage.

8. How do you implement unit tests for LangChain chains?