Skill Guide

Prompt engineering and LLM application architecture (RAG, agents, fine-tuning)

The discipline of designing, optimizing, and integrating LLM capabilities-including prompt construction, retrieval-augmented generation (RAG), autonomous agent orchestration, and model fine-tuning-to build robust, scalable, and context-aware applications.

This skill directly translates unstructured data and complex user intents into automated, high-value business processes, drastically reducing operational latency and creating defensible product moats. It enables organizations to move beyond simple chatbots to intelligent systems that reason over proprietary knowledge, execute multi-step tasks, and continuously improve from domain-specific data.

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How to Learn Prompt engineering and LLM application architecture (RAG, agents, fine-tuning)

Focus on foundational LLM API mechanics (OpenAI, Anthropic, Gemini), basic prompt structuring (system/user/assistant roles, few-shot examples), and understanding core concepts like temperature, token limits, and embeddings. Build a simple single-turn chatbot using a public API as your first project.

Move to practical RAG implementation using frameworks like LangChain or LlamaIndex, focusing on chunking strategies, vector store integration (Pinecone, Weaviate, Chroma), and retrieval evaluation. Develop a stateful agent with tool use (e.g., code execution, web search) and learn to diagnose and mitigate hallucinations, context window limits, and latency issues.

Master complex, multi-agent systems with sophisticated orchestration (e.g., using AutoGen, CrewAI), advanced fine-tuning techniques (LoRA, QLoRA) for domain adaptation, and production-grade deployment. This includes designing evaluation pipelines, implementing guardrails for safety and compliance, and aligning LLM application architecture with specific business KPIs and data security policies.

Practice Projects

Beginner

Project

Build a Domain-Specific FAQ Bot with Basic RAG

Scenario

Create a chatbot that can answer user questions about a specific product's documentation (e.g., a PDF of a software manual) by retrieving relevant text chunks.

How to Execute

1. Pre-process the documentation into text chunks. 2. Generate vector embeddings for each chunk using an API (e.g., text-embedding-3-small). 3. Store these vectors in a simple in-memory or local vector store (e.g., Chroma). 4. For each user query, perform a similarity search to find the top 3 relevant chunks, then construct a prompt that includes these chunks as context and ask the LLM to generate an answer.

Intermediate

Project

Develop a Multi-Tool Research Agent

Scenario

Build an agent that can autonomously research a topic by searching the web, extracting information from specific URLs, and synthesizing findings into a structured report.

How to Execute

1. Define the agent's goal and toolkit (e.g., functions for web search (Tavily), URL scraping (BeautifulSoup)). 2. Implement a ReAct-style loop using LangChain or the OpenAI function calling API, where the agent reasons, selects a tool, executes it, and observes the result. 3. Add memory management to handle conversation history and intermediate findings. 4. Implement output parsing to ensure the final report follows a desired schema (JSON, markdown).

Advanced

Project

Architect a Fine-Tuned, Guardrailed Customer Support System

Scenario

Design and deploy a production system for a financial services company that handles sensitive customer queries, requires strict compliance, and must output precise, structured data for downstream CRM integration.

How to Execute

1. Curate and validate a high-quality, domain-specific dataset of (query, ideal_response) pairs. 2. Fine-tune a base model (e.g., Llama 3 8B) using QLoRA for cost-effective adaptation, monitoring for overfitting. 3. Architect a pipeline: input classifier -> RAG retrieval from knowledge base -> fine-tuned model for response generation -> output validator/guardrail model. 4. Implement robust evaluation using both automated metrics (BLEU, ROUGE) and human-in-the-loop testing for accuracy, safety, and tone. 5. Deploy with a scalable inference server (vLLM, TGI) and comprehensive logging/monitoring.

Tools & Frameworks

Orchestration Frameworks

LangChain/LangGraphLlamaIndexHaystack

Used for chaining LLM calls, managing RAG pipelines, and defining agent workflows. LangChain is the most pervasive; LlamaIndex specializes in data indexing and retrieval; Haystack is strong for end-to-end NLP pipelines.

Vector Databases & Embedding Models

PineconeWeaviateChromaOpenAI text-embedding-3-small/largeBGE-M3

Core infrastructure for RAG. Vector databases store and efficiently query high-dimensional embeddings. The choice between OpenAI's proprietary models and open-source models like BGE-M3 involves trade-offs between cost, performance, and data privacy.

Fine-Tuning & Hosting

Hugging Face Transformers/PEFTQLoRAAxolotlvLLMText Generation Inference (TGI)

Transformers/PEFT and QLoRA are essential for parameter-efficient fine-tuning. vLLM and TGI are high-performance inference servers for deploying fine-tuned models at scale with features like PagedAttention.

Agent Tooling & Frameworks

OpenAI Function Calling / Tool UseAutoGenCrewAILangGraph

OpenAI's native tool use is the standard for structured tool invocation. AutoGen and CrewAI provide higher-level abstractions for creating multi-agent systems that can collaborate and delegate tasks. LangGraph is used for defining complex, stateful agent workflows with cycles.

Interview Questions

Answer Strategy

The interviewer is testing architectural depth and understanding of RAG failure modes. Structure the answer: 1) Chunking Strategy: emphasize semantic chunking over fixed-size, using document structure (sections, paragraphs). 2) Retrieval & Generation: use a two-stage retrieval (hybrid search - keyword + vector) for precision, then pass strict source attribution instructions in the system prompt. 3) Anti-Hallucination: implement a 'faithfulness' evaluator (e.g., using an LLM to check if the answer is fully supported by the retrieved context) before presenting the final answer. 4) Evaluation: mention metrics like Recall@K for retrieval and Exact Match for answers.

Answer Strategy

This tests debugging skills and understanding of the full ML lifecycle. The candidate should outline: 1) Symptom identification (e.g., increased latency, incorrect formats, specific failure cases). 2) Diagnostic steps: comparing training data distribution vs. production input, checking for data leakage, analyzing model confidence scores. 3) Resolution: might involve data augmentation for underrepresented cases, adjusting the fine-tuning objective, or adding a post-processing rule or guardrail. 4) Key principle: stressing the importance of robust, real-world evaluation datasets before deployment.