AI Agent Systems for Engineers

Apply Few-Shot prompting and Chain-of-Thought (CoT) techniques to guide model reasoning.

Manage context windows effectively by calculating token counts and implementing context-trimming strategies.

Enforce structured LLM outputs using JSON mode and explicit schema definitions.

Implement robust error handling for LLM APIs, including exponential backoff for rate limits and provider fallbacks.

Compare LLM inference providers based on cost, latency, and capability trade-offs for production use cases.

Describe the concept of fine-tuning an LLM, its primary use cases, and its operational requirements.

Implement fixed-size and recursive character text chunking strategies for document ingestion.

Implement semantic chunking by leveraging sentence-aware splitting techniques.

Generate vector embeddings for text chunks using a modern embedding model.

Index vector embeddings into a vector database for efficient retrieval.

Perform similarity search and vector retrieval using a vector database.

Implement hybrid search by combining dense vector retrieval with keyword-based search (e.g., BM25).

Improve retrieval relevance by transforming user queries into optimized search queries using an LLM.

Improve retrieval coverage by expanding user queries with generated sub-questions.

Apply a re-ranking model to re-order retrieved contexts for maximum relevance.

Evaluate the retrieval component of a RAG system using metrics like context precision and context recall.

Evaluate the generation component of a RAG system using metrics like faithfulness and answer relevance.

Describe the ReAct (Reasoning and Acting) pattern for agentic workflows.

Build a single-purpose agent using native LLM tool-calling (function calling) capabilities.

Design a multi-agent workflow featuring task routing and agent delegation.

Implement state and memory management across long-running agent interactions.

Design a general agent error policy, including retries and human-in-the-loop fallbacks.

Implement input validation and error handling for tool calls to manage hallucinations or malformed arguments.

Implement a turn/step limit to prevent and handle infinite loops in agentic workflows.

Evaluate agent autonomy and task completion rates using deterministic test cases.

Explain the architecture, lifecycle, and purpose of the Model Context Protocol (MCP).

Define the core API endpoints for an MCP-compliant server for context retrieval and metadata.

Implement a basic MCP server that serves static, hard-coded data in response to a context request.

Connect the MCP server to a local data source (e.g., a file or a simple database) for dynamic context retrieval.

Configure a host application to support the Model Context Protocol and connect an LLM client.

Implement security and access control boundaries within an MCP integration.

Implement semantic caching to reduce API costs and improve response latency.

Configure server-sent events (SSE) to stream LLM responses to a client application.

Design an asynchronous architecture for long-running agentic tasks using a message queue.

Implement a producer to enqueue an agentic task and a consumer (worker) to dequeue and execute it.

Manage task state and results for an asynchronous agentic workflow using a persistent store.

Integrate an LLMOps platform (e.g., Langfuse, Helicone) to monitor token usage, latency, and user feedback.

Describe common prompt injection and jailbreak techniques and their impact on LLM applications.

Implement input filtering and instruction-based defenses to mitigate direct prompt injection attacks.

Implement a moderation endpoint or a 'sandwich' defense to detect and block malicious user inputs.

Implement robust output validation and parsing to mitigate indirect prompt injection from retrieved data or tool outputs.

Learn a structured framework for deconstructing AI system design interview questions.

Apply the framework to outline a solution for a common system design problem, such as a personalized content recommendation engine.

Diagram a highly available, scalable RAG architecture suitable for a whiteboard interview.

Perform a mock system design for a multi-agent customer support platform.

Evaluate and articulate the trade-offs between RAG, fine-tuning, and prompt engineering in interview scenarios.

Structure answers to behavioral questions by strategically leveraging the course projects as AI experience.

Pitch a specific, implementable enhancement to an existing AI system architecture to demonstrate forward-thinking innovation.