---
title: "Complete Beginner's Guide to LLM Observability and Evaluation"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Complete Beginner's Guide to LLM Observability and Evaluation

## Introduction: What Are We Trying to Do?

Imagine you've built an AI application that answers customer questions. How do you know if it's working well? How do you make it better? This guide will teach you exactly that, starting from the very beginning.

---

## Part 1: Understanding Observability (Seeing What Your App Does)

### What is Observability?

**Simple Definition:** Observability means being able to see and understand what your AI application is doing when it runs.

Think of it like this: If your AI application were a person doing a job, observability would be like having a security camera that records everything they do. You can go back and watch to see what happened.

### Why Do We Need Observability?

When you ask an AI application a question, many things happen behind the scenes:

- The application receives your question
- It might call an AI model
- It might search through documents
- It might use tools or functions
- Finally, it gives you an answer

Without observability, you're flying blind. You only see the final answer, but not how the application got there.

---

## Step 1: Understanding Basic Building Blocks

### What is a Run?

A **run** is a single action or step your application takes.

**Example:** Imagine making a sandwich:

- Taking out bread = 1 run
- Spreading butter = 1 run
- Adding cheese = 1 run
- Putting bread slices together = 1 run

Each of these individual actions is a "run."

In AI applications:

- Calling an AI model = 1 run
- Searching documents = 1 run
- Formatting a prompt = 1 run

**Key Point:** A run represents the smallest unit of work in your application.

---

### What is a Trace?

A **trace** is the complete story of everything that happened from start to finish.

**Using Our Sandwich Example:**

- The entire process of making the sandwich (all 4 steps together) = 1 trace
- Each individual step = 1 run within that trace

In AI applications:

- User asks a question → Application searches documents → Calls AI model → Returns answer
- This entire sequence = 1 trace
- Each arrow represents a run within that trace

**Key Point:** A trace is a collection of runs that shows the complete journey from input to output.

---

### What is a Project?

A **project** is a container that holds many traces together.

**Think of it like:**

- A project = A filing cabinet
- Traces = Individual folders in that cabinet
- Runs = Documents inside each folder

**Example:**

- You might have a "Customer Support Bot" project
- Inside it, you have traces for every customer question answered
- Each trace contains all the runs for that specific question

**Key Point:** Projects help you organize and group related traces together.

---

## Step 2: Additional Concepts for Better Organization

### Tags

**Tags** are labels you attach to runs to categorize them.

**Real-World Analogy:** Tags are like sticky notes you put on files to organize them.

**Examples:**

- Tag runs with "urgent" for high-priority requests
- Tag runs with "spanish" for Spanish language questions
- Tag runs with "product-question" for product-related queries

**Why Tags Matter:**

- Easy to find specific types of runs later
- Can filter and analyze by tag
- Helps identify patterns

---

### Metadata

**Metadata** is additional information stored as key-value pairs.

**Think of it like:** The details on a package label:

- `customer_id`: "12345"
- `timestamp`: "2025-10-09 14:30"
- `version`: "v2.1"
- `region`: "north-america"

**Why Metadata Matters:**

- Store context about each run
- Filter runs by specific attributes
- Track versions and environments

---

### Feedback

**Feedback** is a score or rating you give to a run.

**Simple Example:** Like rating a restaurant:

- ⭐⭐⭐⭐⭐ = 5 stars (excellent)
- ⭐⭐ = 2 stars (poor)

**In AI Applications:**

- User clicks "thumbs up" → feedback score = 1
- User clicks "thumbs down" → feedback score = 0
- Or use numerical scores like 0.85 (85% quality)

**Types of Feedback:**

1. **User Feedback:** Real users rate the responses
2. **Automatic Feedback:** Your code automatically scores outputs
3. **Manual Feedback:** You or your team review and score responses

**Key Point:** Feedback tells you how well your application is performing.

---

## Part 2: Understanding Evaluation (Checking How Well Your App Works)

### What is Evaluation?

**Simple Definition:** Evaluation is the process of testing your AI application to see how well it performs.

**Real-World Analogy:**

- Building an app = Teaching someone a job
- Evaluation = Giving them a test to see if they learned

### Why Evaluate?

1. **Know if it works:** Does your app actually answer questions correctly?
2. **Find problems:** Where is your app making mistakes?
3. **Measure improvements:** Is your new version better than the old one?
4. **Build confidence:** Can you trust your app in production?

---

## Step 3: Core Evaluation Concepts

### Datasets: Your Test Collection

A **dataset** is a collection of examples you use to test your application.

**Think of it like:** A practice test with questions and answers:

```
Question 1: What is the capital of France?
Expected Answer: Paris

Question 2: How do I reset my password?
Expected Answer: Click on 'Forgot Password' link...

```

### What's in a Dataset?

Each example in a dataset has:

1. **Inputs:** The question or prompt you give your application
    - Example: "What are your business hours?"
2. **Reference Outputs (optional):** The correct answer you expect
    - Example: "We're open Monday-Friday, 9 AM to 5 PM"
3. **Metadata (optional):** Extra information about the example
    - Example: `category: "hours"`, `difficulty: "easy"`

### Why Datasets Matter

- **Consistency:** Test your app the same way every time
- **Comparison:** Compare different versions of your app
- **Coverage:** Make sure you test different types of situations

---

### Building Your First Dataset

**Step-by-Step Process:**

### Method 1: Manual Creation

Start small! Create 10-20 examples by hand:

```
Example 1:
Input: "How do I return a product?"
Expected Output: "You can return products within 30 days..."
Category: "returns"

Example 2:
Input: "What payment methods do you accept?"
Expected Output: "We accept credit cards, PayPal, and..."
Category: "payment"

```

**Tip:** Focus on:

- Common questions you expect
- Edge cases (unusual situations)
- Known problem areas

### Method 2: Use Real Data

Once your app is running:

- Collect actual user questions
- Pick interesting or problematic examples
- Add them to your dataset

### Method 3: Synthetic Generation

Once you have some examples:

- Use AI to generate similar examples
- Expand your dataset faster
- Create variations of existing examples

---

### Evaluators: Your Grading System

An **evaluator** is a function that scores how well your application performed on a test.

**Think of it like:** A teacher grading homework. The evaluator looks at:

- What question was asked (input)
- What answer your app gave (output)
- What the correct answer should be (reference, if available)

### Types of Evaluators

**1. Heuristic Evaluators (Rule-Based)**

These follow simple, fixed rules.

**Examples:**

- Check if the answer is not empty
- Check if the answer contains certain keywords
- Check if the answer matches expected format
- Measure response length

**Code Example (Conceptual):**

```python
def check_not_empty(output):
    if len(output) > 0:
        return {"score": 1, "comment": "Answer provided"}
    else:
        return {"score": 0, "comment": "Empty answer"}

```

**When to Use:**

- Simple checks
- Fast evaluation
- Clear pass/fail criteria

---

**2. LLM-as-Judge Evaluators**

Use another AI model to grade the output.

**How it Works:**

1. Give an AI model the question and answer
2. Ask it to evaluate quality based on criteria
3. AI returns a score and explanation

**Example Prompt to AI Judge:**

```
Question: "What are your business hours?"
Answer: "We're open every day from 9 to 5"
Reference Answer: "Monday-Friday, 9 AM to 5 PM EST"

Evaluate if the answer is factually accurate compared to the reference.
Give a score from 0-1 and explain why.

```

**When to Use:**

- Complex questions
- Need nuanced judgment
- Comparing meaning, not exact words

---

**3. Human Evaluators**

Real people review and score outputs.

**Process:**

1. Show humans the question and answer
2. They rate quality (thumbs up/down or 1-5 stars)
3. Scores are collected and analyzed

**When to Use:**

- Most accurate for subjective tasks
- When starting out to understand quality
- For final validation of important changes

---

### Experiments: Testing Your Application

An **experiment** is when you run your application on an entire dataset and collect the results.

**Think of it like:** Giving a complete exam to a student and seeing how they do overall.

### What Happens in an Experiment?

1. **Setup:** You have a dataset with 100 test questions
2. **Run:** Your application answers all 100 questions
3. **Evaluate:** Each answer gets scored by your evaluators
4. **Analyze:** You see overall performance and identify problems

### Example Experiment Results:

```
Dataset: Customer Support Questions (50 examples)
Average Score: 0.82 (82%)
- 41 questions answered correctly
- 9 questions answered incorrectly
- Common issue: Wrong information about shipping

```

---

## Step 4: Types of Evaluation

### Offline Evaluation

**What it is:** Testing your application on prepared test data before deploying it.

**Real-World Analogy:** Like a dress rehearsal before the real performance.

**Process:**

1. Create a dataset
2. Run your application on it
3. Evaluate the results
4. Fix problems
5. Repeat until satisfied

**When to Use:**

- Before deploying a new version
- Testing changes to your app
- Comparing different approaches

**Pros:**

- Safe (not affecting real users)
- Controlled environment
- Repeatable

**Cons:**

- Limited to test examples
- May miss real-world issues

---

### Online Evaluation

**What it is:** Evaluating your application's outputs while it's running in production with real users.

**Real-World Analogy:** Like a security guard watching cameras in real-time to spot problems.

**How it Works:**

1. User asks a question
2. App generates an answer
3. Evaluator checks the answer immediately
4. Flag issues or score quality
5. Collect feedback

**Example Checks:**

- Is the response appropriate?
- Does it contain any harmful content?
- Is it relevant to the question?

**When to Use:**

- Monitoring live applications
- Catching unexpected problems
- Understanding real-world performance

**Pros:**

- Catches real issues
- Immediate feedback
- Real user data

**Cons:**

- Can be expensive
- Need fast evaluators
- Harder to debug

---

### Pairwise Evaluation

**What it is:** Comparing two different versions of your application side-by-side.

**Real-World Analogy:** Like a taste test comparing Coke vs. Pepsi.

**Example:**

```
Question: "What's your return policy?"

Version A Answer: "Returns accepted within 30 days with receipt."
Version B Answer: "You can return items within 30 days. Keep your receipt!"

Judge: Which answer is more helpful and friendly?
Result: Version B is better (more detailed and friendly)

```

**When to Use:**

- Comparing old vs. new versions
- Testing different prompts
- Choosing between AI models

**Why it's Useful:**

- Sometimes easier than absolute scoring
- Humans are good at comparisons
- Helps pick the best option

---

## Step 5: Common Evaluation Scenarios

### Scenario 1: Testing Answer Correctness

**Goal:** Check if your app gives correct information.

**What You Need:**

- Dataset with questions and correct answers
- Evaluator that compares actual vs. expected answers

**Example:**

```
Input: "What is 2 + 2?"
Expected: "4"
Actual: "4"
Score: 1.0 (Correct!)

Input: "What is the capital of France?"
Expected: "Paris"
Actual: "The capital is Lyon"
Score: 0.0 (Wrong!)

```

---

### Scenario 2: Testing Response Quality

**Goal:** Check if answers are helpful, even without a "correct" answer.

**What You Need:**

- Dataset with questions
- LLM-as-judge evaluator with quality criteria

**Example Criteria:**

- Is the answer relevant?
- Is it clear and understandable?
- Is it complete?
- Is tone appropriate?

---

### Scenario 3: Finding Regressions

**Goal:** Make sure new changes don't break things that worked before.

**Process:**

1. Run evaluation on current version (Baseline)
2. Make changes to your app
3. Run evaluation again on new version
4. Compare: Did scores go down? (That's a regression!)

**Example:**

```
Baseline Version: 90% correct answers
New Version: 75% correct answers
⚠️ REGRESSION DETECTED! New version is worse.

```

---

## Step 6: Best Practices for Beginners

### Start Small

**Don't do this:** Create 1000 test examples on day one
**Do this:** Start with 10-20 carefully chosen examples

**Why:**

- Easier to manage
- Faster to iterate
- Learn what matters

### Begin with Simple Evaluators

**Don't do this:** Build complex AI judges immediately
**Do this:** Start with basic checks:

- Is response empty?
- Does it contain required keywords?
- Is it the right length?

**Why:**

- Easier to understand
- Faster to run
- Less can go wrong

### Look at Your Data

**Don't do this:** Only look at aggregate scores
**Do this:** Read actual examples:

- Look at questions that failed
- Understand why they failed
- Learn patterns

**Why:**

- Numbers don't tell the full story
- You'll spot real issues
- Better intuition for improvements

### Iterate Frequently

**Don't do this:** Make many changes then test
**Do this:** Make one change, test, repeat

**Why:**

- Know what caused improvements/problems
- Faster learning
- Less overwhelming

---

## Step 7: Putting It All Together

### The Complete Workflow

**Phase 1: Setup (Do Once)**

1. Add observability to your application
2. Create a small initial dataset (10-20 examples)
3. Define 1-2 simple evaluators

**Phase 2: Baseline (Do Once)**
4. Run your current application on the dataset
5. Look at results and scores
6. Identify problem areas

**Phase 3: Improve (Repeat)**
7. Make one improvement to your app
8. Run evaluation again
9. Compare to baseline
10. If better, make it the new baseline
11. If worse, undo the change

**Phase 4: Expand (Over Time)**
12. Add more examples to your dataset
13. Add more sophisticated evaluators
14. Run online evaluation in production
15. Use real user feedback

---

## Common Mistakes to Avoid

### Mistake 1: No Observability

**Problem:** You can't see what your app is doing
**Solution:** Add tracing from day one

### Mistake 2: Too Few Examples

**Problem:** Test scores don't represent real performance
**Solution:** Gradually grow your dataset with diverse examples

### Mistake 3: Only Automated Evaluation

**Problem:** Miss nuanced issues that only humans catch
**Solution:** Regularly review examples manually

### Mistake 4: Ignoring Edge Cases

**Problem:** App fails in unusual situations
**Solution:** Specifically test edge cases in your dataset

### Mistake 5: Not Tracking Changes

**Problem:** Don't know what made things better or worse
**Solution:** Keep notes on what changed between experiments

---

## Quick Reference: Key Terms

| Term | Simple Definition |
| --- | --- |
| **Run** | A single step or action in your application |
| **Trace** | The complete journey from input to output (collection of runs) |
| **Project** | A container organizing related traces |
| **Tag** | A label to categorize runs |
| **Metadata** | Extra information stored as key-value pairs |
| **Feedback** | A score or rating on a run's quality |
| **Dataset** | Collection of test examples (inputs and expected outputs) |
| **Evaluator** | Function that scores how well your app performed |
| **Experiment** | Running your app on an entire dataset and collecting results |
| **Offline Evaluation** | Testing on prepared data before production |
| **Online Evaluation** | Evaluating outputs in real-time with real users |
| **Pairwise Evaluation** | Comparing two versions side-by-side |

---

## Your First Week Action Plan

### Day 1: Understanding

- Read this guide completely
- Understand runs, traces, and projects
- Don't code yet, just understand concepts

### Day 2: Observation

- Add basic observability to your app
- Run it a few times
- Look at the traces

### Day 3: First Dataset

- Create 5 simple test examples manually
- Write down inputs and expected outputs

### Day 4: First Evaluator

- Write a simple evaluator (check if output is not empty)
- Test it manually on one example

### Day 5: First Experiment

- Run your 5 examples through your app
- Use your simple evaluator
- Calculate average score

### Day 6: Analysis

- Look at which examples failed
- Understand why
- Write down patterns

### Day 7: First Improvement

- Make one small change to improve results
- Run the experiment again
- Compare scores

---

## Conclusion

Evaluation and observability might seem complex at first, but remember:

1. **Start simple** - Begin with basic concepts and tools
2. **Practice regularly** - Run small experiments frequently
3. **Learn from failures** - Failed tests teach you the most
4. **Expand gradually** - Add complexity only when needed
5. **Stay curious** - Keep asking "why did that happen?"

The goal isn't perfection on day one. The goal is continuous improvement through systematic observation and testing.
