Skill Guide

Prompt engineering and LLM orchestration using frameworks like LangChain and LlamaIndex

The discipline of designing inputs and orchestrating interactions with large language models (LLMs) using frameworks to build reliable, scalable applications.

It transforms LLMs from unpredictable text generators into dependable, integrated business tools, directly impacting automation efficiency, cost reduction, and new product velocity.

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How to Learn Prompt engineering and LLM orchestration using frameworks like LangChain and LlamaIndex

1. Master prompt fundamentals: zero-shot, few-shot, chain-of-thought (CoT) prompting. 2. Learn core LangChain components: Models, Prompts, Chains, Memory, Indexes. 3. Implement basic retrieval-augmented generation (RAG) with a vector store like FAISS.

1. Move beyond simple chains to agents with tools (e.g., using ReAct framework). 2. Implement advanced memory types (summary, entity) for stateful applications. 3. Debug using LangSmith traces to identify prompt failures and latency bottlenecks. Avoid prompt injection by sanitizing user input.

1. Architect complex, multi-agent systems using frameworks like AutoGen or CrewAI. 2. Implement fine-tuning pipelines (LoRA, QLoRA) for domain-specific model adaptation. 3. Build evaluation frameworks using tools like LangChain Evaluators or custom metrics to rigorously test LLM system reliability and accuracy before deployment.

Practice Projects

Beginner

Project

Build a Document Q&A Bot

Scenario

Create a bot that answers questions based solely on the content of uploaded PDF documents, without using external knowledge.

How to Execute

1. Use LlamaIndex's SimpleDirectoryReader to load documents. 2. Create a vector index with a chosen embedding model (e.g., OpenAI text-embedding-ada-002). 3. Configure a query engine with a custom prompt that instructs the LLM to answer only from the provided context. 4. Build a simple Streamlit/Gradio UI for interaction.

Intermediate

Project

Implement a Multi-Tool Research Agent

Scenario

Build an agent that can research a topic by searching the web, querying a SQL database of reports, and summarizing findings, then compiling a report.

How to Execute

1. Define custom tools for web search (via Tavily API) and a SQL database query tool using LangChain's SQLDatabase tool. 2. Initialize a zero-shot-react-description agent with these tools. 3. Implement a memory buffer to maintain conversation history across multiple research steps. 4. Add output parsing to structure the final report in Markdown format.

Advanced

Project

Production RAG System with Guardrails

Scenario

Deploy a customer support RAG system for a financial product that must cite sources, handle sensitive data, and refuse to answer when uncertain.

How to Execute

1. Build a modular RAG pipeline using LlamaIndex's ingestion and query pipelines. 2. Integrate guardrails: a) Use NeMo Guardrails for conversational topics, b) Implement a prompt layer with role-based filtering (e.g., block financial advice generation). 3. Add a citation module that traces answers to specific document chunks. 4. Set up automated evaluation with a test suite for hallucination detection and performance monitoring.

Tools & Frameworks

Core Orchestration Frameworks

LangChainLlamaIndexHaystack

LangChain provides a wide, flexible component library for general-purpose chains and agents. LlamaIndex specializes in data ingestion and indexing for RAG. Haystack offers a production-oriented, pipeline-based architecture. Use LlamaIndex for data-heavy RAG, LangChain for complex agent workflows, and Haystack for enterprise-grade deployment.

Model Providers & APIs

OpenAI APIAnthropic APIGoogle Gemini APIAzure OpenAI Service

Direct APIs for accessing LLMs (GPT-4, Claude, Gemini). Use the official SDK or LangChain wrappers. For enterprise, use Azure/AWS Bedrock for compliance, managed keys, and integrated monitoring.

Vector Databases & Storage

FAISSChromaDBPineconeWeaviate

FAISS (in-memory) for prototyping. ChromaDB for lightweight, persistent local storage. Pinecone/Weaviate for managed, scalable vector storage in production. The choice depends on scalability, latency, and operational overhead requirements.

Evaluation & Debugging

LangSmithRagasDeepEval

LangSmith provides tracing, debugging, and monitoring for LangChain. Ragas/DeepEval offer metrics for RAG quality (faithfulness, relevance). Use these tools in CI/CD pipelines to automatically evaluate LLM application performance.

Interview Questions

Answer Strategy

The interviewer is testing systematic debugging skills and deep framework knowledge. Use the LATS (Language Agent Tree Search) or tracing approach. 'First, I would enable verbose logging and check the AgentExecutor's thought/action loop in the trace (e.g., in LangSmith). The key is to examine the 'intermediate_steps' list to see the action input and observation for each iteration. Often, the issue is a poorly defined tool description causing the LLM to misunderstand its function, or a lack of explicit stopping criteria. I'd refine the tool's description and add a max_iterations parameter to the executor.'

Answer Strategy

Testing problem-solving and user-centric thinking beyond technical correctness. 'I would implement a dual evaluation framework. First, technical: use metrics like faithfulness and answer relevance from Ragas to ensure correctness. Second, human-centric: create a sampled evaluation dataset with 'golden answers' graded for helpfulness. I'd A/B test prompt variations that inject user intent (e.g., 'Explain like I'm a new customer') and add a 'why this helps' synthesis step to the RAG pipeline, using the LLM to connect the factual answer to the user's likely underlying goal.'