Skill Guide

AI and LLM fundamentals: transformer architecture, prompt engineering, fine-tuning, RAG, agents

The foundational knowledge required to build, customize, and deploy applications using Large Language Models (LLMs), encompassing the core Transformer neural network architecture, the art of crafting effective inputs (prompt engineering), the process of adapting pre-trained models to specific domains (fine-tuning), techniques for grounding models in external knowledge (Retrieval-Augmented Generation - RAG), and the design of autonomous AI systems that can reason and act (agents).

This skill set is highly valued as it directly enables the creation of intelligent, context-aware automation and decision-support systems, transforming business processes from static workflows into adaptive, knowledge-driven operations. The impact is measured in accelerated innovation cycles, enhanced customer/product personalization, and the ability to unlock value from unstructured data at scale.

1 Careers

1 Categories

9.0 Avg Demand

25% Avg AI Risk

How to Learn AI and LLM fundamentals: transformer architecture, prompt engineering, fine-tuning, RAG, agents

Start with understanding the core components of the Transformer model (self-attention, encoder-decoder structure) via seminal papers and visual explainers. Grasp the difference between base LLMs and instruction-tuned models. Practice basic prompt engineering by systematically testing different phrasing, roles, and constraints on a single model (e.g., OpenAI's API) to solve a defined task.

Move to implementation by fine-tuning a smaller open-source model (like Llama 2 7B or Mistral 7B) on a curated, domain-specific dataset using frameworks like Hugging Face Transformers. Build a functional RAG pipeline by integrating a vector database (e.g., Pinecone, Weaviate) with an LLM to answer questions based on a private document set. Focus on evaluating retrieval accuracy and answer hallucination rates.

Architect multi-agent systems using frameworks like LangChain or CrewAI where agents with specialized roles (researcher, coder, critic) collaborate on complex tasks. Design and implement sophisticated fine-tuning strategies like RLHF (Reinforcement Learning from Human Feedback) or DPO (Direct Preference Optimization) to align model behavior with nuanced human preferences. Develop comprehensive evaluation frameworks that measure model performance across accuracy, latency, cost, and safety metrics for production deployment.

Practice Projects

Beginner

Project

Build a Personal Knowledge Q&A Bot

Scenario

You have a collection of 10-15 personal notes or articles in PDF/Markdown format on a specific topic (e.g., 'machine learning papers', 'investment strategies'). You want to ask natural language questions and get answers sourced directly from these documents.

How to Execute

1. Use a tool like LangChain or LlamaIndex to split the documents into semantic chunks. 2. Generate vector embeddings for each chunk using an embedding model (e.g., OpenAI's text-embedding-ada-002 or a local model like all-MiniLM-L6-v2). 3. Store embeddings in a simple vector store (e.g., FAISS or ChromaDB). 4. Create a query interface that retrieves the top 3-5 relevant chunks and passes them, along with the user's question, as context to an LLM for final answer generation.

Intermediate

Project

Fine-Tune a Code Assistant for a Proprietary API

Scenario

Your company has a complex, undocumented internal API. You need to create a code assistant that can generate correct Python client code for developers based on natural language requests, using the API's specific authentication, endpoints, and data models.

How to Execute

1. Create a high-quality dataset: Compile 500-1000 examples of (natural language request, correct Python code) pairs, ensuring they cover key API patterns and edge cases. 2. Select a base model (e.g., CodeLlama 7B). 3. Use the Hugging Face `trl` library with `SFTTrainer` to perform Supervised Fine-Tuning on your dataset. 4. Evaluate the fine-tuned model's output on a held-out test set for syntactic correctness and functional accuracy against a mock API endpoint.

Advanced

Project

Design a Multi-Agent Research & Report System

Scenario

Develop a system that can autonomously research a given topic, synthesize findings from multiple sources, critique its own work, and produce a structured, cited report, simulating the workflow of a research team.

How to Execute

1. Design agent roles: 'Researcher' (uses search tools and RAG to gather info), 'Analyst' (synthesizes and structures key points), 'Critic' (checks for logical consistency and biases), 'Writer' (composes the final report). 2. Implement using an agent framework (e.g., CrewAI, AutoGen). Define the tasks, goals, and communication protocols between agents. 3. Integrate tools: web search API, PDF reader, RAG retrieval system. 4. Implement a memory system (e.g., shared vector store) for agents to store and retrieve intermediate findings, and a validation loop where the 'Critic' agent can send work back for revision.

Tools & Frameworks

LLM Development Frameworks

LangChainLlamaIndexHugging Face Transformers

LangChain is used for building complex, tool-using agent chains and RAG pipelines. LlamaIndex is optimized for data indexing and retrieval (RAG). Hugging Face Transformers is the core library for accessing, training, and fine-tuning thousands of open-source models.

Vector Databases & Embedding Models

PineconeWeaviateChromaDBOpenAI Embeddingssentence-transformers

Vector databases store and efficiently retrieve vector embeddings for RAG. Embedding models (OpenAI API or local sentence-transformers) convert text into numerical vectors that capture semantic meaning for similarity search.

Agent Frameworks & Orchestration

CrewAIAutoGen (Microsoft)LangGraph

These frameworks provide structured ways to define agent personas, goals, and collaborative workflows, managing the state and communication between multiple LLM calls and tool uses.

Model Training & Fine-Tuning Platforms

Hugging Face TRL/SFT TrainerAxolotlWeights & Biases (W&B)

TRL and Axolotl simplify the process of Supervised Fine-Tuning (SFT) and alignment (RLHF/DPO). W&B is essential for tracking experiment metrics, model performance, and system resource usage during training.

Interview Questions

Answer Strategy

Structure your answer by comparing them on key dimensions: cost (RAG/Prompt eng. lower upfront), latency (Prompt eng. potentially faster), data requirements (Fine-tuning needs curated data, RAG needs a knowledge base), and core capability change (Fine-tuning changes model behavior, RAG adds knowledge, Prompt eng. guides existing behavior). Sample: 'For a task requiring new, proprietary knowledge like legal precedent lookup, RAG is superior as it avoids catastrophic forgetting and is easier to update. For changing the model's style or reasoning process, like teaching it a company's coding standards, fine-tuning is effective. Prompt engineering is best for quick optimization of response format or tone with a known model, but is brittle and less effective for deep domain adaptation.'

Answer Strategy

This tests your understanding of the RAG pipeline and evaluation. Break it down into two main failure points: retrieval and generation. Sample: 'First, I'd isolate the issue by inspecting the retrieved context chunks for each bad query. If the retrieval is poor (missing relevant docs), I'd improve the chunking strategy, try a hybrid search (keyword + semantic), or fine-tune the embedding model. If retrieval is good but generation is bad, I'd refine the prompt template to more strongly instruct the model to use the provided context and add citations. I would also implement a confidence scoring mechanism, like checking if the answer is entailed by the context, and fall back to a safe response when confidence is low.'