Skill Guide

LLM application architecture (RAG, agents, function calling, fine-tuning workflows)

The discipline of designing and implementing production-grade systems that integrate large language models with external data sources, tools, and custom logic to solve complex, real-world tasks.

This skill directly translates to building AI products that generate revenue, automate knowledge work, and create competitive advantages by moving beyond chatbots into autonomous, context-aware agents. It is the core engineering competency separating proof-of-concept demos from scalable, enterprise-ready AI solutions.

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25% Avg AI Risk

How to Learn LLM application architecture (RAG, agents, function calling, fine-tuning workflows)

1. **Core Architecture Patterns**: Internalize the key distinctions and use cases for Retrieval-Augmented Generation (RAG), Agentic Loops, and Fine-Tuning. 2. **Fundamental Tooling**: Get hands-on with LangChain or LlamaIndex to build basic RAG and agent prototypes. 3. **API Proficiency**: Master the function/tool calling APIs of major providers (OpenAI, Anthropic, Google) to understand structured output and action execution.

Transition from tutorials to building internal tools. Focus on: 1. **RAG Pitfalls**: Implement advanced chunking, embedding strategies, and re-ranking to fix accuracy issues. 2. **Agent Robustness**: Design error handling, state management, and fallback mechanisms within agentic loops. 3. **System Integration**: Use function calling to connect your LLM application to live databases, CRMs, or internal APIs. Avoid building overly complex agents for simple tasks; start with a deterministic workflow.

Master the system design and operational layers. Focus on: 1. **Hybrid Architectures**: Architect systems that combine a fine-tuned model for core domain tasks, RAG for knowledge retrieval, and agents for multi-step reasoning. 2. **Cost & Latency Optimization**: Implement model cascading, caching, and observability (tracing, logging) to run at scale. 3. **Strategic Alignment**: Mentor teams on choosing the right architectural pattern (RAG vs. Fine-Tuning vs. Agent) based on business requirements, data privacy, and cost constraints.

Practice Projects

Beginner

Project

Internal Knowledge Base Q&A Bot

Scenario

A company's product documentation is scattered across Markdown files and Confluence. Build a bot that can accurately answer employee questions about product features and procedures.

How to Execute

1. **Ingest & Chunk**: Use LlamaIndex or LangChain's document loaders to parse files. Experiment with different chunk sizes and overlap (e.g., 512 tokens, 128 overlap). 2. **Embed & Index**: Use a sentence-transformer model (e.g., all-MiniLM-L6-v2) to create vector embeddings and store them in a vector database like Chroma or FAISS. 3. **Build the Chain**: Construct a retrieval-augmented generation chain that fetches relevant chunks and feeds them as context to a simple prompt for the LLM. 4. **Deploy**: Wrap it in a Streamlit or Gradio UI for basic interaction.

Intermediate

Project

Automated Sales Report Agent

Scenario

A sales manager needs a system that can query a live SQL database, analyze trends, and generate a summary report based on natural language requests like 'Show me Q3 pipeline for the APAC region and highlight the top 3 accounts.'

How to Execute

1. **Define Tools**: Create a Python function that executes SQL queries and another that can format data into a table. Use Pydantic to define their schemas. 2. **Integrate with Function Calling**: Use OpenAI's function calling to map the LLM's intent to these Python functions. The agent should decide when to query the database and how to interpret the results. 3. **Add a Reasoning Loop**: Implement a ReAct-style agent that can reason about the SQL query output, decide if it's sufficient, and formulate a final natural language answer. 4. **Handle Errors**: Build in safeguards for SQL injection and gracefully handle queries that return no data.

Advanced

Project

Multi-Modal Customer Support Agent

Scenario

A tech company wants an AI agent that can handle customer support tickets involving text descriptions, error screenshots, and logs. The agent must diagnose issues, use internal tools (ticketing system, knowledge base), and escalate complex cases to humans.

How to Execute

1. **Architect the Agent Core**: Design a central agent (e.g., using AutoGen or a custom state machine) that can manage conversation history and task state. 2. **Integrate Specialized Models**: Use a vision model (like GPT-4V) to extract text from screenshots, then pass that context to the main reasoning agent. 3. **Orchestrate Tools**: Give the agent access to multiple tools: a RAG system for the knowledge base, a function to create Jira tickets, and a function to fetch user history from a CRM. 4. **Implement Guardrails & Escalation**: Define clear logic for when the agent's confidence is low, requiring it to summarize the issue and route the ticket to a human agent via a tool call. 5. **Deploy with Observability**: Use tools like LangSmith or Phoenix to trace every step of the agent's reasoning and tool usage for debugging and improvement.

Tools & Frameworks

Orchestration Frameworks

LangChainLlamaIndexHaystackAutoGen

These are the primary scaffolds for building RAG pipelines and agent loops. LangChain and LlamaIndex are the de facto standards for rapid prototyping and production. Use Haystack for complex, customizable NLP pipelines. Use AutoGen for multi-agent conversation frameworks.

Vector Databases & Embeddings

PineconeWeaviateChromaFAISSSentence-Transformers

Chroma and FAISS are excellent for local development and small-to-medium scale. Pinecone and Weaviate are managed, scalable vector DBs for production. Sentence-Transformers (Hugging Face) provides the models to generate the embeddings stored in these databases.

Observability & Evaluation

LangSmithPhoenix (Arize)RagasDeepEval

Critical for moving beyond 'vibe checks'. LangSmith and Phoenix provide tracing for agent reasoning and RAG retrieval. Ragas and DeepEval are frameworks for quantitatively evaluating RAG system metrics like context relevance, faithfulness, and answer correctness.

Deployment & APIs

FastAPIStreamlitGradioOpenAI Function CallingAnthropic Tool Use

FastAPI is the industry standard for building robust, scalable backend APIs for your LLM application. Streamlit/Gradio are for rapid internal tool and demo UIs. The provider-specific tool calling APIs are essential for structured, reliable action execution.

Interview Questions

Answer Strategy

This tests depth of knowledge beyond basic implementation. Structure your answer around the RAG pipeline stages: **1. Retrieval Diagnosis**: Verify chunk quality (are chunks too large/small?), embedding model suitability, and re-ranking effectiveness. Use tools like Ragas to check context precision/recall. **2. Generation Diagnosis**: Analyze the prompt template. Is it explicitly instructing the model to use the context? Is it asking for a specific format (e.g., 'quote the relevant passage')? **3. Iterative Fix**: Implement a fix at the weakest point. For retrieval: try a different chunking strategy (e.g., parent-child). For generation: add chain-of-thought prompting or require citations. Re-evaluate with a hold-out test set.

Answer Strategy

This assesses strategic thinking and architectural judgment. The key factors are **data specificity, task type, cost, latency, and control**. Use a clear framework: **Fine-tuning** when the task requires a consistent, specialized output style/format, deep domain knowledge is static, and you have high-quality labeled data. **RAG** when knowledge is dynamic, you need citations, and the task is primarily about retrieval and synthesis. **Agents** when the task requires multi-step reasoning, interacting with external systems, and dynamic decision-making.