☰ Page Navigation
LLM Benchmarking
Understanding Model Evals, Standardized Benchmarks, Custom Evaluation, and the Eight Core LLM Capabilities.
LLM evaluation has two broad layers: Model Evals, which directly test an LLM's capabilities, and Application Evals, which test an LLM-powered application such as a RAG system or agent. This page moves from application-level evaluation into the model itself.
The central idea is simple: if an AI Engineer cannot measure a model's capabilities, it becomes difficult to choose the right model, compare new releases, assess safety, or justify deployment decisions.
Measures an LLM's capabilities and behavior.
Standardized shared test for model comparison.
Evaluation built around your actual task/data.
Eight major capability areas organize most benchmarks.
On This Page
1. Why Do AI Engineers Need Model Evals?
Model Evals exist to measure what an LLM can actually do. For an AI Engineer, however, the important question is not simply “Why do frontier labs evaluate models?” but “Why do I need model evaluation when my job is to build applications?”
Reason 1 — Compare Models and Select the Right One
Suppose a company is building a RAG application and must choose between two LLM providers. In a professional engineering discussion, “both are good” is not enough. The team needs concrete evidence showing which model performs better on the capabilities that matter for the application.
Model Evals provide comparable scores that support a model-selection decision.
Reason 2 — Track Whether New Models Actually Improve
Imagine an application is running on one model and a newer model is released. A manager may ask whether the team should migrate. Model evaluation provides numbers that can be used to compare the old and new models rather than relying on marketing claims or intuition.
Reason 3 — Evaluate Safety and Reliability
Model Evals can test whether a model is safe enough for a particular use case. The lecture specifically highlights concerns such as hallucination, jailbreak susceptibility, and responsible behavior.
Reason 4 — Decide Between Proprietary APIs and Self-Hosting
An AI Engineer may need to decide between using a proprietary model through an API and deploying an open-source model on company infrastructure. Capability, cost, and practical suitability must be compared. Model evaluation helps make this decision evidence-based.
2. What Is a Model Eval?
A Model Eval is a systematic process of measuring an underlying model's capabilities, behavior, reliability, and operational characteristics under controlled conditions.
Simple Explanation
A model eval is essentially an exam for an LLM. You decide what capability you want to test, give the model a defined test, run it under controlled conditions, and then score and interpret the result.
Why There Is No Single “LLM IQ” Number
LLMs are general-purpose systems with many different capabilities. There is no single evaluation that fully describes all of them. Reasoning, coding, mathematics, long-context handling, multimodal understanding, tool use, safety, and instruction following can require different tests.
3. The Four-Step Model Evaluation Process
Step 1 — Decide What to Test
Choose the capability: reasoning, coding, safety, instruction following, or another target capability.
Step 2 — Bring a Test
Select or construct a test mechanism that measures the chosen capability. This can be a standardized benchmark or a custom evaluation set.
Step 3 — Run the Model Under a Fixed Protocol
Fix the relevant conditions so that the evaluation is repeatable. When comparing multiple models, comparable conditions are essential.
Step 4 — Score and Interpret
After the test, calculate the score and interpret what it means for model capability and the intended decision.
4. Two Types of Model Tests
| Type | What It Is | Primary Purpose |
|---|---|---|
| Benchmark | A standardized, shared test used across models. Examples mentioned in the lecture include MMLU and SWE-bench. | Compare models on common ground and understand general capabilities. |
| Custom Evaluation | A dataset/test assembled from the actual task the application cares about. | Measure suitability for a specific application rather than generic usefulness. |
5. Case Study — Why Custom Evals Matter
Consider an application that reads incoming emails and routes them into categories such as Billing, Technical, and Refund. Two model choices are available:
| Property | Model A | Model B |
|---|---|---|
| Model profile | Large, powerful, top-of-leaderboard | Smaller, cheaper, mid-table on public benchmarks |
| Illustrative token cost | ~$15 / 1M tokens | ~$0.50 / 1M tokens |
| Classification accuracy | 94% | 91% |
| Urgency accuracy | 88% | 87% |
| Illustrative latency | ~4.1 s | ~9 s |
The lecture's example then proposes building a golden dataset from roughly 200–500 historical emails, labeling each email according to the application's target categories, and evaluating both models on exactly that task.
Although Model A wins on generic public benchmarks, Model B is only slightly behind on this particular application task while being substantially cheaper. The conclusion is therefore that Model B can be the better value proposition for this specific workload.
6. The Eight Core LLM Capabilities
LLMs are general-purpose models. They can perform text generation, sentiment analysis, summarization, part-of-speech tagging, coding, and many other tasks. Because there is no single score that captures every capability, benchmarks are organized around specific capability areas.
Knowledge & Reasoning
Factual knowledge and the ability to connect multiple facts to reach conclusions.
Coding & Software Engineering
Code generation, debugging, testing, repository-level work, commands, and tool/API interaction.
Mathematics
Accurate symbolic and numerical reasoning across difficulty levels.
Long Context
Effective use of information contained in very large inputs.
Vision & Multimodal
Understanding images, videos, and information beyond text.
Agentic & Tool Use
Using tools, browsing, APIs, structured calls, and computer environments to perform actions.
Safety & Alignment
Responsible behavior, resistance to harmful/adversarial use, truthfulness, and security-related behavior.
Instruction Following
Following user-specified constraints, formats, styles, lengths, and clarification requirements.
7. Capability 1 — Knowledge & Reasoning
Definition
This capability combines the amount of factual knowledge a model has with its ability to connect facts and reason over them.
What Is Measured?
- Factual recall: knowledge across areas such as biology, physics, chemistry, and history.
- Multistep logical reasoning: connecting multiple facts in the correct sequence to reach a conclusion.
MMLU
The lecture cites MMLU as a benchmark evaluating models across 57 subjects.
Example
A task asking the model to connect a long chain of historical or scientific events and explain how those events contributed to the structure of modern society tests both factual knowledge and reasoning.
Why It Matters
Strong performance is treated as an indicator of how intelligent or capable a model is. It is particularly relevant to research assistants, technical document analysis, professional assistants, and complex question answering.
8. Capability 2 — Coding & Software Engineering
This capability evaluates whether an LLM can perform useful programming and real software-engineering work, not merely produce code that looks plausible.
What Is Measured?
- Generate functional code from natural-language requirements.
- Generate test cases.
- Improve code based on failing tests or errors.
- Find and fix bugs in an existing codebase.
- Perform multi-file and long-horizon engineering tasks.
- Refactor an entire codebase based on a requirement.
- Run multiple command-line operations.
- Install packages, configure servers, and set up environments.
- Use APIs and function calls effectively.
9. Capability 3 — Mathematics
Mathematics can be viewed as a structured form of reasoning in which a sequence of operations leads to a solution.
Levels Mentioned
- Grade-school mathematics.
- Competition-level problem solving, including problems requiring creative thinking.
- Undergraduate mathematics.
- Research-level mathematical reasoning, including difficult or open-ended problems.
Real-World Relevance
Mathematical capability matters in scientific computing, financial modeling, engineering simulations, and data analysis.
10. Capability 4 — Long Context
Long-context evaluation measures whether a model can effectively use information from very large inputs, potentially containing hundreds of thousands of tokens.
What Is Tested?
- Retrieve a small fact from a very large context.
- Find details about a specific person or entity in a large document.
- Summarize very large inputs.
- Maintain and use the context of a large codebase when acting as a coding agent.
11. Capability 5 — Vision & Multimodal
This capability moves beyond text and evaluates whether the model can understand information such as images and videos.
Why It Matters
Real-world interaction is multimodal. Examples include asking a model what can be prepared from items visible inside a refrigerator or asking it to identify a particular book from a visual environment.
Evaluation Focus
- Image understanding
- Video understanding
- Cross-modal reasoning
12. Capability 6 — Agentic & Tool Use
Agentic AI requires models that can do more than generate text. They must interact with tools and external environments.
What Is Tested?
- Web browsing.
- Structured tool calling.
- API interaction.
- Computer or desktop interaction.
- Executing multi-step actions using available tools.
This capability is increasingly important as agentic applications become more common.
13. Capability 7 — Safety & Alignment
Safety and alignment evaluate whether a model can be trusted to behave responsibly.
Areas Mentioned in the Lecture
- Harmful-content generation.
- Resistance to adversarial attacks.
- Truthfulness versus excessive agreeableness or sycophancy.
- Cybersecurity-related skills and behavior.
- Capabilities involving cryptography, reverse engineering, and digital forensics.
14. Capability 8 — Instruction Following
Instruction following measures whether the model does what the user requested and follows the requested constraints.
Examples
- If the user requests a bullet list, does the model produce a bullet list?
- If the user asks for fewer than 200 words, does it respect the limit?
- If the user requests a friendly response, does it follow that style?
- If the instruction is ambiguous, does the model ask an appropriate clarifying question?
15. Benchmark vs Custom Evaluation — Final Comparison
| Dimension | Benchmark | Custom Evaluation |
|---|---|---|
| Test design | Standardized/shared | Designed around your task |
| Primary goal | General capability comparison | Application-specific suitability |
| Comparability | High across models | High within your controlled setup |
| Example | MMLU, SWE-bench | Historical labeled application data |
| Best question answered | “How good is this model generally?” | “Which model is best for our workload?” |
16. Interview Questions
A systematic process for measuring an underlying model's capabilities, behavior, reliability, and operational characteristics under controlled conditions.
To compare models, select the right model, assess new model releases, evaluate safety/reliability, and support decisions such as API usage versus self-hosting.
Select the capability → bring a test → run under a fixed protocol → score and interpret.
A standardized shared test used to compare models on common ground.
Because public benchmarks measure generic capabilities. A custom eval measures performance on the actual task, data, constraints, cost, and latency requirements of your application.
Knowledge & Reasoning; Coding & Software Engineering; Mathematics; Long Context; Vision & Multimodal; Agentic & Tool Use; Safety & Alignment; Instruction Following.
It makes evaluation repeatable and ensures that models being compared are tested under comparable conditions.
If it provides sufficiently similar task performance while reducing cost and/or other operational burdens, it can be a better value proposition for the workload.
17. Quick Revision
- Model Evals directly test LLM capabilities; Application Evals test LLM-powered applications.
- AI Engineers need Model Evals for model comparison, selection, release tracking, safety assessment, and deployment strategy.
- A Model Eval follows four steps: Capability → Test → Fixed Protocol → Score & Interpret.
- Two major test approaches are standardized benchmarks and custom evaluations.
- Benchmarks provide common-ground comparison; custom evals provide application-specific evidence.
- The lecture's Zomato example demonstrates why benchmark leadership does not automatically mean best application fit.
- The eight core capabilities are: Knowledge & Reasoning, Coding & SWE, Mathematics, Long Context, Vision & Multimodal, Agentic & Tool Use, Safety & Alignment, Instruction Following.
- There is no single universal “LLM IQ” score that captures every capability.
- Long context should be evaluated by actual information use, not only by the advertised context-window size.
- Instruction following is important because it directly affects user experience and product trust.
Think Like an AI Engineer
Do not ask only, “Which model is strongest?” Ask: “Which model performs well on the capabilities my application actually needs, under the cost and operational constraints I have?” Benchmarks help you understand general capability; custom evaluations help you make the final application decision.