Skill Guide

AI/LLM Workflow Design (prompt engineering, RAG, agents for data retrieval)

AI/LLM Workflow Design is the architectural practice of structuring sequences of prompts, retrieval mechanisms, and agent-based logic to orchestrate complex, reliable tasks using large language models.

This skill directly translates unstructured business knowledge into executable AI systems, automating expert-level analysis and decision support. It reduces time-to-insight from days to seconds for data-intensive workflows, creating a defensible competitive advantage.

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How to Learn AI/LLM Workflow Design (prompt engineering, RAG, agents for data retrieval)

Focus on 1) Mastering prompt engineering fundamentals (zero-shot, few-shot, chain-of-thought). 2) Understanding the core components of RAG: indexing, retrieval, and generation. 3) Learning basic agent tool-use patterns (e.g., using a search API).

Move to designing multi-step workflows. Practice debugging failures in RAG pipelines (e.g., poor retrieval recall vs. generation hallucination). Develop evaluation metrics for your workflows beyond simple accuracy. Common mistake: over-engineering a single prompt instead of decomposing the task.

Master the design of autonomous agent loops (e.g., ReAct, Plan-and-Solve) with guardrails. Architect systems for cost/latency optimization (e.g., model cascading, caching strategies). Align workflow design with business KPIs and lead cross-functional teams in implementation.

Practice Projects

Beginner

Project

Build a FAQ Bot for Internal Documentation

Scenario

Your company's HR and IT policies are in 50+ PDFs. New employees need quick answers.

How to Execute

1. Use LangChain or LlamaIndex to build a basic RAG pipeline over the PDFs. 2. Implement a simple similarity search retriever (e.g., using FAISS). 3. Craft a system prompt that instructs the LLM to answer based only on the provided context. 4. Deploy a minimal front-end using Streamlit for testing.

Intermediate

Project

Design a Multi-Source Research Agent

Scenario

A market analyst needs to synthesize information from SEC filings, earnings call transcripts, and news articles to answer a complex query.

How to Execute

1. Design an agent with tools for each data source (e.g., EDGAR API, Whisper for transcripts, news API). 2. Implement a planning step (e.g., using Chain-of-Thought) to decompose the user's question. 3. Build a retrieval step with a re-ranker to filter low-relevance documents. 4. Implement a verification step where the agent checks its final answer against source citations.

Advanced

Project

Architect a Self-Improving Customer Support Workflow

Scenario

A SaaS company wants to reduce support ticket volume by 40% using an AI agent that learns from new documentation and past resolutions.

How to Execute

1. Design a hybrid RAG system combining a vector store for semantic search and a knowledge graph for structured relationships. 2. Implement an 'evaluator' agent that scores the quality of the 'solver' agent's responses against a rubric. 3. Create a feedback loop where low-confidence answers are flagged for human review, and the reviewed answers are ingested back into the knowledge base. 4. Instrument the system with observability to track cost-per-resolution and customer satisfaction impact.

Tools & Frameworks

Software & Platforms

LangChainLlamaIndexHaystack

Core orchestration frameworks for building RAG pipelines and agents. Use LangChain for broad tool integration, LlamaIndex for advanced data indexing strategies, and Haystack for production-ready, component-based pipelines.

Vector Databases & Retrieval

PineconeWeaviateFAISSCohere Rerank

Use Pinecone or Weaviate for managed, scalable vector storage. Use FAISS for local, high-performance similarity search. Integrate Cohere Rerank or a cross-encoder to dramatically improve retrieval precision after initial recall.

Mental Models & Methodologies

Chain-of-Thought (CoT) PromptingReAct FrameworkOODA Loop (Observe-Orient-Decide-Act) for Agents

CoT is mandatory for complex reasoning. ReAct is the foundational pattern for creating agents that reason and act. Use the OODA loop as a design framework to structure agent decision-making cycles for high-stakes tasks.

Interview Questions

Answer Strategy

Demonstrate a structured, component-level diagnostic approach. 'I would isolate the retrieval and generation stages. First, I'd sample queries where the answer was wrong and manually inspect if the relevant document was in the top-k retrieved results. If not, the issue is retrieval (embeddings, chunking, or the query). If the correct context was retrieved but the answer was wrong, the issue is in the generation prompt or the LLM's instruction following. I'd then test hypotheses like improving the retrieval re-ranking or adding more specific prompt instructions with constraints.'

Answer Strategy

Testing for systems thinking and prioritization. 'In a lead scoring agent, we needed sub-2-second latency. I framed the problem with a 2x2 matrix: high/low accuracy need vs. high/low cost sensitivity. For the final scoring, we used a fine-tuned, smaller model (high accuracy, lower cost). For the initial data gathering from web sources, we used a faster, cheaper model with a human-in-the-loop review for the top 10% of leads. This prioritized latency for the user flow while managing cost and accuracy where it mattered most.'