Skill Guide

AI/ML fundamentals including prompt engineering, RAG pipelines, and LLM fine-tuning

The practical mastery of interacting with, augmenting, and customizing large language models (LLMs) through structured prompting, retrieval-augmented generation (RAG) architectures, and supervised fine-tuning to build domain-specific AI systems.

This skill is highly valued because it directly translates to building more accurate, context-aware, and cost-effective AI applications that solve specific business problems, reducing reliance on generic API calls. It impacts business outcomes by enabling the creation of proprietary AI solutions that enhance productivity, improve customer experience, and provide a competitive moat.

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How to Learn AI/ML fundamentals including prompt engineering, RAG pipelines, and LLM fine-tuning

1. Core LLM Concepts: Understand transformer architecture basics, tokenization, temperature, and top-p sampling. 2. Prompt Engineering Fundamentals: Master zero-shot, few-shot, and chain-of-thought (CoT) prompting using direct API calls (e.g., OpenAI API). 3. Basic RAG Understanding: Learn the difference between parametric knowledge (in the model) and non-parametric knowledge (in a vector database).

1. RAG Pipeline Construction: Implement a full RAG pipeline using a framework like LangChain or LlamaIndex, focusing on chunking strategies, embedding models (e.g., text-embedding-ada-002), and vector stores (e.g., Pinecone, FAISS). 2. Fine-tuning Workflow: Execute a supervised fine-tuning (SFT) run on a smaller model (e.g., a 7B parameter model) using curated instruction datasets, focusing on data preparation and hyperparameter tuning. 3. Evaluation & Iteration: Move beyond accuracy to implement domain-specific evaluation metrics (e.g., faithfulness, relevancy) and use A/B testing frameworks.

1. System Architecture & Optimization: Design hybrid systems that intelligently route queries between RAG, fine-tuned models, and the base LLM based on complexity and cost. 2. Advanced Fine-tuning Techniques: Implement parameter-efficient fine-tuning (PEFT) methods like LoRA/QLoRA and Direct Preference Optimization (DPO) for alignment. 3. MLOps for LLMs: Establish CI/CD pipelines for model training, versioning of datasets and prompts, and continuous monitoring for drift, performance, and safety in production.

Practice Projects

Beginner

Project

Build a Q&A Bot Over Your Own Documents

Scenario

You have a collection of 20 PDF technical manuals for an internal product. You need to create a bot that can answer specific questions about those manuals.

How to Execute

1. Data Preparation: Use a library like PyPDF2 to extract text and split it into semantically meaningful chunks. 2. Embedding & Indexing: Use an embedding model (e.g., from Hugging Face's sentence-transformers) to convert chunks into vectors and store them in a local FAISS index. 3. Pipeline Assembly: Use a simple Python script to take a user query, embed it, find the top-k relevant chunks via FAISS, and feed them as context to an LLM API (e.g., GPT-3.5-turbo) for a final answer.

Intermediate

Project

Fine-tune a Model for a Specialized Task

Scenario

Your company's customer support team needs an AI that can classify incoming support tickets into one of 10 specific technical categories with high accuracy, something a base LLM does poorly.

How to Execute

1. Dataset Curation: Collect and clean 1000+ examples of support tickets with their correct labels. Format them into an instruction-following JSONL dataset (e.g., {'instruction': 'Classify this ticket...', 'input': '...', 'output': 'Category X'}). 2. Fine-tuning: Use a platform like Hugging Face's `trl` library with a SFTTrainer to fine-tune a base model (e.g., Mistral-7B) on this dataset. Use a validation set to monitor for overfitting. 3. Evaluation & Deployment: Test the fine-tuned model on a held-out test set to measure precision/recall. Deploy the model via a simple FastAPI endpoint, comparing its latency and cost against a prompting-only solution.

Advanced

Project

Architect a Self-Improving Customer Service Agent

Scenario

You are tasked with building a customer service agent that not only answers questions from a knowledge base (RAG) but also learns from successful human agent resolutions to improve over time.

How to Execute

1. Hybrid Architecture: Build a router that first attempts to answer via a RAG pipeline over the FAQ/knowledge base. If confidence is low, it routes to a human. 2. Feedback Loop & RLHF: Log all interactions with human ratings. Use highly-rated human agent responses to create a DPO dataset. Periodically fine-tune the RAG's reader model on this data to align it with human-preferred answers. 3. Continuous Evaluation: Implement an automated evaluation pipeline that runs nightly on a curated test suite to track performance across dimensions (accuracy, tone, hallucination rate) and triggers alerts or retraining workflows.

Tools & Frameworks

Core Libraries & Frameworks

LangChain / LlamaIndexHugging Face Transformers / PEFT / TRLOpenAI API / Anthropic SDK

Use LangChain/LlamaIndex for rapid RAG pipeline prototyping and chain orchestration. Use Hugging Face ecosystem for model fine-tuning, PEFT (LoRA), and RLHF/DPO implementation. Use cloud APIs (OpenAI, Anthropic) for accessing state-of-the-art base models and simple fine-tuning endpoints.

Vector Databases & Embeddings

FAISSPineconeWeaviateSentence-Transformers

FAISS for local, high-performance similarity search prototyping. Pinecone/Weaviate for managed, scalable production vector databases. Sentence-Transformers for generating high-quality text embeddings for RAG retrieval.

Evaluation & Monitoring

RAGASDeepEvalPhoenix by Arize AI

RAGAS and DeepEval for automated evaluation of RAG pipelines (faithfulness, relevancy). Use Phoenix or similar observability platforms to trace, debug, and monitor LLM calls in production, tracking latency, cost, and quality metrics.

Infrastructure & MLOps

Weights & Biases (W&B)MLflowDockervLLM / TGI

W&B or MLflow for experiment tracking during fine-tuning. Docker for containerizing model serving endpoints. Use optimized inference servers like vLLM or TGI for high-throughput, cost-efficient model serving in production.

Interview Questions

Answer Strategy

Structure your answer around the pipeline stages: 1. Data Ingestion (chunking, cleaning), 2. Indexing (embedding, vector store), 3. Retrieval (similarity search, hybrid search), 4. Generation (prompt construction, LLM call). Highlight failure points: poor chunking strategy leading to loss of context, retrieval misses due to poor embeddings or query rewriting, and hallucination generation where the model ignores retrieved context. A strong answer mentions evaluation metrics like faithfulness and recall@k.

Answer Strategy

The interviewer is testing your methodological rigor and understanding of alignment. Strategy: 1. Diagnosis: Use evaluation tools (e.g., DeepEval) to quantify the hallucination rate and identify failure patterns. 2. Data-Centric Approach: Use techniques like RLHF or, more practically with limited data, DPO. Collect pairs of outputs (good vs. bad) from human experts to create a preference dataset. 3. Implementation: Fine-tune the model using DPO, which directly optimizes the model to prefer the 'good' response over the 'bad' one, improving alignment with factual correctness without requiring massive amounts of new data.