# QFLOO

> AI engineering courses by Raju Kancharla on LLM evaluation, retrieval, inference and serving, and agents. Hands-on labs, and free lessons in every course.

## Courses

- [Advanced Python](https://qfloo.com/courses/advanced-python): Predict what CPython does with a line of code, choose a concurrency model with measured numbers, and profile and speed up a real program with results you can defend.
- [AI Agents: What They Are and How to Build One](https://qfloo.com/courses/ai-agents-fundamentals): Build a small tool-using agent from scratch, know where agents break, and know when a fixed workflow is the better choice.
- [AI Security and Red-Teaming](https://qfloo.com/courses/ai-security): Threat-model an AI application, break it with prompt injection and tool abuse in a safe lab, apply layered defences, measure attack success with error bars, and respond to an incident.
- [Build Your First AI Application](https://qfloo.com/courses/build-your-first-ai-app): Ship a small AI application that has a test set, error handling and a cost limit, built step by step as a support-email triage tool.
- [Master Competitive Programming](https://qfloo.com/courses/competitive-programming): Solve contest problems with a tested template, a stress-test habit and a toolkit of proven patterns, and run a practice routine built from your own weakness list.
- [Deep Dive into Databases](https://qfloo.com/courses/database-internals): Explain and predict how a database stores, indexes, plans, isolates, logs and replicates data, diagnose slow queries and bad plans, run a safe schema change, and build a small storage engine with a write-ahead log and tested crash recovery.
- [Deep Learning and Transformers from Scratch](https://qfloo.com/courses/deep-learning-transformers): Build, train and evaluate a small decoder-only transformer from raw array code, and explain each part (backpropagation, optimiser, attention, scaling, fine-tuning) with numbers from your own runs.
- [Agent Reliability Engineering](https://qfloo.com/courses/evals-agents): Build an eval harness that tells you, with numbers and error bars, whether a change made your agent better or worse, and blocks the release when it is worse.
- [How AI Is Built: From Data to a Working Model](https://qfloo.com/courses/how-ai-is-built): Follow one model from raw data to deployment, know what each stage costs and where it goes wrong, train, evaluate, release and serve a small model yourself, and see how the same ideas scale to language models.
- [Inference and Serving Engineering](https://qfloo.com/courses/inference-serving): Predict, measure and improve the latency, throughput and cost of serving a language model, and plan capacity for a target, using numbers from your own runs.
- [LLM Performance Engineering](https://qfloo.com/courses/llm-performance): Cut the cost and p95 latency of an LLM feature with measured changes, and prove quality did not drop.
- [Agent Tooling and MCP Engineering](https://qfloo.com/courses/mcp-tool-design): Design, build, test and secure a tool server that an agent can use correctly and that cannot be turned against its users.
- [ML Mathematics for AI Engineers](https://qfloo.com/courses/ml-mathematics): Compute, check and explain the mathematics inside a model (shapes, gradients, likelihoods, entropy and floating point error) well enough to debug a training run from the numbers.
- [Retrieval Engineering](https://qfloo.com/courses/production-rag): Build a retrieval-augmented system whose retrieval and answers are measured, so a change to chunking, ranking or data cannot quietly make it worse.
- [Reading AI Research Papers](https://qfloo.com/courses/reading-ai-papers): Decide whether a paper's evidence supports its claim, rebuild its equations and cost numbers, read the papers behind a technical decision in a defensible order, and reproduce one result with an honest account of the gap.
- [System Design for AI Products](https://qfloo.com/courses/system-design-ai): Design an AI product end to end (requirements, capacity and cost, architecture, agents, retrieval, serving, queues, caching, reliability, tenancy, safety, evaluation and observability) and defend the design in a written document with numbers.
- [What Is AI? A Clear Introduction](https://qfloo.com/courses/what-is-ai): Explain what AI, machine learning and large language models are, what they can and cannot do, decide whether a task suits AI, and run a small honest evaluation of your own.
- [Working with LLMs: Prompts, Context and Limits](https://qfloo.com/courses/working-with-llms): Get dependable results from an LLM, know when it is guessing, and keep a personal test set that catches regressions when you change a prompt or a model.

## AlgoRank

- [AlgoRank](https://qfloo.com/algorank): original practice problems judged in a sandbox for Python 3 and C++, with measured time and memory. 246 problems today; the statements of the free ones are public, the rest are in the paid plan.
- [Cut To Match](https://qfloo.com/algorank/problems/anagram-gap) (free)
- [Template Tag Audit](https://qfloo.com/algorank/problems/balanced-tags) (free)
- [Water Stop Gap](https://qfloo.com/algorank/problems/closest-gap) (free)
- [Audit Window](https://qfloo.com/algorank/problems/ledger-ranges) (free)
- [Two Orphan Parts](https://qfloo.com/algorank/problems/odd-pair) (free)
- [Ticket Range Count](https://qfloo.com/algorank/problems/seat-search) (free)
- [Crane Pairs](https://qfloo.com/algorank/problems/shelf-pairs) (free)
- [Decorated Slots](https://qfloo.com/algorank/problems/tile-floor) (free)

## Founder

Raju Kancharla is the founder of QFLOO and designs its courses.
- He has built many startups and worked across companies of various sizes.
- The courses are designed by him together with expert friends at large technology companies, who review the lessons.
- LinkedIn: https://www.linkedin.com/in/rajukancharla
- GitHub: https://github.com/rajukancharla21
- Contact: raju@qfloo.com

## Pages

- [Courses](https://qfloo.com/courses): All courses
- [Pricing](https://qfloo.com/pricing): Plans and how they work
- [FAQ](https://qfloo.com/#faq): Common questions
- [Privacy](https://qfloo.com/privacy): How QFLOO collects, uses and protects your personal data.
- [Terms](https://qfloo.com/terms): The terms for using QFLOO.
- [Refund policy](https://qfloo.com/refunds): QFLOO sales are final. Read how plans, cancellation, billing mistakes and invoices work.

## Contact

- Raju Kancharla, raju@qfloo.com

## Company

- Legal name: QFLOO AI PRIVATE LIMITED
- CIN: U70200AP2025PTC117413
- Registered address: 7-6-569, Dhanama Nilayam, Rajeev Gandhi Nagar, Guntur, Andhra Pradesh 522002, India