Master LLM Evaluations: The Step-by-Step Playlist for 2026 | New Playlist | CampusX

Summary

CampusX introduces a playlist on LLM evaluations (LLM evals) for AI engineers who need to assess whether LLM-based products are reliable enough to deploy—not just build working demos. The presenter argues that evaluation is an important industry skill and a common interview topic, especially for RAG and agentic systems.

Why informal “vibe testing” falls short

The video calls casually asking an application a few questions and judging its answers by feel “vibe testing.” This approach is subjective, informal, and difficult to repeat consistently. It may be adequate for a personal project, but it is not a dependable basis for launching a production system.

Three examples illustrate the risks:

• Air Canada chatbot: It reportedly gave incorrect guidance about bereavement fares, telling a customer to pay full price and seek a refund later. The customer was later denied a refund under the actual policy, and Air Canada was held responsible for its website chatbot.
• Chevrolet dealership chatbot: A user reportedly manipulated a dealer’s chatbot into agreeing to a $1 car offer. The offer was not honored, but the exchange attracted negative attention online.
• Fake legal citations: A lawyer reportedly relied on ChatGPT-generated, nonexistent cases in court without verifying them. According to the video, the lawyer and firm were fined about $5,000.

The presenter’s takeaway is that LLM applications should be systematically evaluated before deployment.

Why LLM evaluation is difficult

The video contrasts LLM evaluation with traditional software testing:

1. LLMs are probabilistic: The same prompt may produce different valid answers, unlike a deterministic program such as a calculator.
2. Correctness is not the only criterion: Evaluating an LLM application may involve several dimensions, including factuality, completeness, tone, groundedness, latency, and cost. The relevant criteria vary by application.

Playlist roadmap

The presenter outlines a sequence covering:

1. What LLM evaluation is, with examples.
2. The broader landscape of evaluation techniques and tools.
3. How to evaluate LLMs, including categories of benchmarks used to compare models.
4. How to evaluate LLM-powered applications.
5. Building an evaluation pipeline, including a golden dataset and custom evaluation rubrics.
6. RAG-specific evaluation.
7. Agent evaluation.
8. Safety evaluation.
9. Operational evaluation after deployment, including latency, time to first token, tokens per second, and system load.

The video frames the playlist as a way to move from building LLM projects toward designing systems that can serve large numbers of users reliably. It also mentions CampusX’s earlier AI-engineering coverage, including LangChain, RAG chatbots, agents, frameworks, LangSmith, prompt engineering, and no-code tools.

Reviews, guides, or tutorials

This is an introductory playlist-launch and roadmap video, not a hands-on evaluation tutorial. It explains the motivation for LLM evals and previews the topics that later videos will teach.

Main speaker/source

• Nitish — presenter and host of the CampusX video.