Skill Guide

LLM application architecture: prompt engineering, RAG pipelines, agent frameworks, and tool-use patterns

LLM application architecture encompasses the systematic design of systems that integrate large language models with external data, logic, and tools to perform complex, context-aware tasks.

This skill directly impacts business outcomes by enabling the creation of AI-powered products that automate workflows, enhance decision-making, and deliver personalized user experiences. Mastery reduces development time, lowers operational costs, and creates defensible competitive advantages through sophisticated, reliable AI integrations.

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How to Learn LLM application architecture: prompt engineering, RAG pipelines, agent frameworks, and tool-use patterns

Start with the fundamentals: understand prompt engineering basics (zero-shot, few-shot, chain-of-thought), learn vector database concepts and embedding models, and grasp the core agent loop (Perceive, Reason, Act).

Move to implementation: build a RAG pipeline using a framework like LlamaIndex or LangChain, experiment with different chunking and retrieval strategies, and implement a simple tool-calling agent. Common mistakes include poor document preprocessing and failing to evaluate retrieval quality separately from generation.

Focus on architecture and systems design: engineer for reliability (guardrails, fallbacks), implement complex agent orchestration (multi-agent systems, planning), optimize for cost and latency at scale, and align system capabilities with strategic business objectives. Master the trade-offs between different architectural patterns (e.g., monolithic vs. microservice agent frameworks).

Practice Projects

Beginner

Project

Build a RAG-Powered Q&A Bot for a PDF Document

Scenario

You need to create a bot that can answer questions based solely on the content of a provided technical manual or research paper (PDF).

How to Execute

1. Extract text from the PDF using a library like PyMuPDF or pdfplumber. 2. Split the text into manageable chunks (e.g., 512 tokens) with overlapping boundaries. 3. Use an embedding model (e.g., OpenAI `text-embedding-3-small`) to vectorize chunks and store them in a local vector store like ChromaDB. 4. Build a simple retrieval-augmented generation loop: embed the user query, retrieve the top 3-5 relevant chunks, and pass them as context to the LLM (e.g., GPT-3.5-Turbo) with a prompt like "Answer the question based only on the provided context."

Intermediate

Project

Develop an Agent with Tool-Using Capabilities

Scenario

Create an agent that can perform web searches and execute Python code to solve complex, data-driven user requests.

How to Execute

1. Define tools: a function for a search API (e.g., SerpAPI) and a function for a Python code execution sandbox (e.g., using `exec` in a restricted environment or an API like E2B). 2. Use a framework like LangChain's AgentExecutor to create an agent with a system prompt that explains the available tools. 3. Implement a ReAct (Reasoning + Acting) pattern where the LLM generates a "Thought" about what to do, an "Action" (tool call), and observes the result. 4. Test with complex queries like "Find the population of France in 2023 and calculate the square root of that number."

Advanced

Project

Architect a Multi-Agent System for Automated Research

Scenario

Design a system where multiple specialized agents collaborate to research a topic, synthesize findings, and produce a structured report.

How to Execute

1. Define agent roles (e.g., "Researcher" for web search, "Analyst" for data evaluation, "Writer" for synthesis). 2. Implement a hierarchical orchestration pattern where a "Manager" agent decomposes the task and delegates sub-tasks to specialized agents. 3. Use a shared memory blackboard (e.g., a Redis store or a shared JSON object) for inter-agent communication and state management. 4. Implement robust error handling, timeout mechanisms, and a final validation agent to check for coherence and factual grounding in the output report.

Tools & Frameworks

LLM Orchestration Frameworks

LangChainLlamaIndexHaystack

These provide standardized abstractions for chains, agents, and RAG pipelines. Use LangChain for broad agent/tool integration, LlamaIndex for advanced data ingestion and indexing, and Haystack for production-ready search and QA systems.

Vector Databases & Embedding Models

PineconeWeaviateChromaDBOpenAI EmbeddingsSentence-Transformers (SBERT)

Vector databases store and retrieve embeddings for RAG. Use managed services like Pinecone/Weaviate for scale, ChromaDB for prototyping. OpenAI and SBERT are standard embedding model providers; choose based on cost, latency, and domain-specific performance.

Agent & Tool Infrastructure

AutoGenCrewAILangGraphFunction Calling APIs (OpenAI, Anthropic)

Use AutoGen or CrewAI for multi-agent conversation patterns, LangGraph for stateful, cyclic agent workflows. Native function calling APIs from OpenAI/Anthropic provide the foundational mechanism for structured tool use.

Evaluation & Monitoring

RAGASDeepEvalLangSmithPhoenix (Arize AI)

RAGAS and DeepEval provide metrics for RAG pipeline quality (faithfulness, relevancy). LangSmith and Phoenix offer observability, tracing, and debugging for complex LLM application traces in production.

Interview Questions

Answer Strategy

Structure the answer around the pipeline stages: Ingestion (document parsing, chunking strategy, metadata extraction), Indexing (embedding model choice, vector database with filtering, incremental updates), Retrieval (hybrid search combining vector and keyword, reranking), and Generation (prompt engineering with cited context, guardrails). Mention failure modes like poor chunking losing context, stale data, retrieval of irrelevant chunks (low precision), and hallucination despite provided context.

Answer Strategy

The interviewer is testing for problem-solving depth and understanding of system complexity. A strong answer will: 1) Define a concrete task (e.g., "generating a market analysis report"), 2) Explain why a single prompt failed (e.g., required multiple data sources, verification steps, and iterative refinement), 3) Detail the agentic solution (e.g., a planner agent, search agent, analyst agent), and 4) Explicitly discuss trade-offs: increased latency, higher cost, more complex debugging, and the need for careful error handling vs. improved accuracy and capability.