Live Coding Interview Prep

Course Overview
Intermediate
Free Course

For AI and backend engineers facing a live-coding round where they must build LLM features — RAG, agents, API plumbing, chunking, guardrails, evaluation and embedding-based ML — in front of an interviewer. For each of the 50 tasks you get the short approach to say out loud, the concept it rests on, tested Python you can write from memory, a traced example, and answers to the follow-up questions.

Instructor: MantraMindAI
Sections: 7

Course Content

Section 1: RAG & Retrieval Systems

Prepares you to code a retrieval pipeline live — chunking, embedding search, hybrid BM25 plus vector search, re-ranking, query expansion, compression and citations — and to state the complexity of each piece.

1. Build a basic RAG pipeline using embeddings and a vector database.
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2. Implement semantic search using cosine similarity.
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3. Write code for a hybrid search system (keyword + vector search).
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4. Implement a simple re-ranking model for retrieved results.
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5. Build a vector similarity search engine from scratch.
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6. Write cosine similarity, dot product, and Euclidean distance functions manually.
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7. Implement query expansion for better retrieval results.
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8. Build a multi-query RAG system that merges results.
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9. Implement context compression before passing data to the LLM.
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10. Add citation tracking to a RAG pipeline.
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Section 2: Agents & Tool Use

Prepares you to code an agent live — the tool loop, function-calling handlers, stop conditions, routing, memory, retries, planning, reflection and guardrails — and to explain where each one fails in production.

1. Build an AI agent that uses tools like calculator and search.
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2. Implement a function-calling handler for LLM APIs.
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3. Write an agent loop that decides when to stop execution.
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4. Build a multi-agent collaboration system with defined roles.
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5. Implement tool selection logic based on user query.
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6. Add memory (short-term + long-term) to an agent.
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7. Implement retry logic for failed tool calls.
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8. Build a planning + execution agent workflow.
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9. Add reflection (self-correction) to an agent loop.
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10. Implement guardrails for tool usage in agents.
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Section 3: LLM API & Backend Systems

Prepares you to write the backend plumbing around a model call live — streaming, templating, retries, exact and semantic caches, token budgets, prompt versions, provider fallback, rate limits and tracing — and to defend each design choice.

1. Implement streaming responses from an LLM API.
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2. Build a prompt templating system with variable injection.
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3. Implement exponential backoff retry for API calls.
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4. Build a caching layer for LLM responses.
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5. Implement semantic caching for similar queries.
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6. Write token counting and context window management logic.
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7. Build a prompt versioning system.
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8. Implement fallback between multiple LLM providers.
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9. Add rate limiting and throttling to API calls.
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10. Build a logging and tracing system for LLM pipelines.
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Section 4: Data Processing & Chunking

Prepares you to code the text-preparation layer live — fixed, recursive and semantic chunking, PDF extraction, sliding-window and summary memory, and context reordering — and to explain what each one costs and breaks.

1. Write code for fixed-size and recursive text chunking.
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2. Implement semantic chunking based on meaning.
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3. Build a document parser for PDFs and extract text.
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4. Implement sliding window memory for conversations.
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5. Add summarization-based memory to reduce context size.
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6. Handle "lost in the middle" by reordering context chunks.
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Section 5: Evaluation & Guardrails

Prepares you to code the checks around an LLM feature live — judge-based evals, consistency-based hallucination flags, injection detection, output filters, A/B tests and quality, latency and cost metrics — and to say honestly what each one cannot catch.

1. Build an evaluation pipeline using LLM-as-a-judge.
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2. Implement hallucination detection using multiple responses.
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3. Write a prompt injection detection system.
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4. Build output guardrails for filtering unsafe or irrelevant responses.
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5. Implement A/B testing for prompts or models.
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6. Track metrics like accuracy, latency, and cost in code.
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Section 6: Embeddings, Classification & Clustering

Prepares you to code classic ML on top of embeddings live — zero-shot and trained classifiers, confusion matrices and metrics, HDBSCAN clustering, PCA and UMAP reduction, and a BERTopic-style topic pipeline — and to explain why each step is there.

1. Implement zero-shot classification using embeddings.
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2. Build a supervised classifier using embedding features.
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3. Write code to compute confusion matrix and evaluation metrics.
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4. Implement text clustering using embeddings + HDBSCAN.
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5. Apply dimensionality reduction (PCA/UMAP) before clustering.
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6. Build a simple topic modeling pipeline (BERTopic-style).
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Section 7: Advanced & Production-Oriented

Prepares you to design and code a complete LLM application live — the API, database, retrieval, model layer, memory, guardrails and evaluation — and to explain how the pieces fit, fail and get measured.

1. Design a full backend architecture for an LLM app (API + DB + model).
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2. Build an end-to-end chatbot with memory, RAG, and evaluation pipeline.
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