RAG evaluation metrics in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - RAG evaluation metrics

This pipeline shows how a Retrieval-Augmented Generation (RAG) model processes input text by retrieving relevant documents, generating answers, and then evaluating the quality of those answers using specific metrics.

Data Flow - 4 Stages

1Input Text

1 sample x 1 text string→User provides a question or prompt→1 sample x 1 text string

"What is the capital of France?"

↓

2Document Retrieval

1 sample x 1 text string→Retrieve top relevant documents from knowledge base→1 sample x 5 documents (text)

["Paris is the capital of France.", "France is in Europe.", "The Eiffel Tower is in Paris.", "Paris has many museums.", "French cuisine is famous."]

↓

3Answer Generation

1 sample x 5 documents→Generate answer using retrieved documents and input question→1 sample x 1 generated answer string

"The capital of France is Paris."

↓

4Evaluation Metrics Calculation

1 sample x 1 generated answer string + 1 reference answer string→Calculate metrics like Exact Match, F1 Score, and Rouge-L→1 sample x 3 metric scores

{"Exact Match": 1.0, "F1 Score": 1.0, "ROUGE-L": 0.85}

Training Trace - Epoch by Epoch

Loss
1.0 |***************
0.8 |************   
0.6 |********      
0.4 |******        
0.2 |***           
0.0 +-------------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.60	Initial training with moderate loss and accuracy.
2	0.65	0.72	Loss decreased, accuracy improved as model learns retrieval and generation.
3	0.50	0.80	Better alignment between retrieved documents and generated answers.
4	0.40	0.85	Model shows strong retrieval and generation performance.
5	0.35	0.88	Training converges with high accuracy and low loss.

Prediction Trace - 4 Layers

Layer 1: Input Question

Layer 2: Document Retrieval

Layer 3: Answer Generation

Layer 4: Evaluation Metrics Calculation

Model Quiz - 3 Questions

Which stage reduces the input from one question to multiple documents?

A Evaluation Metrics Calculation

B Answer Generation

C Document Retrieval

D Input Text

Key Insight

RAG models combine retrieval and generation steps, and their evaluation metrics like Exact Match and F1 Score help measure how well the generated answers match reference answers. Training shows steady improvement as the model learns to retrieve relevant documents and generate accurate responses.

Practice

1. What does RAG evaluation metrics primarily measure in a retrieval-augmented generation system?

easy

A. Both the quality of generated answers and the relevance of retrieved documents

B. Only the speed of document retrieval

C. The size of the training dataset

D. The number of layers in the neural network

Solution

1. Step 1: Understand RAG system components

   RAG combines document retrieval and answer generation, so evaluation must cover both parts.

2. Step 2: Identify what metrics measure

   Metrics check answer quality (like accuracy) and retrieval quality (like precision).

3. Final Answer:

   Both the quality of generated answers and the relevance of retrieved documents -> Option A

4. Quick Check:

   RAG metrics = answer + retrieval quality [OK]

Hint: RAG means check both answer and retrieval quality [OK]

Common Mistakes:

• Thinking RAG only measures answer quality
• Confusing retrieval speed with quality
• Ignoring document relevance in evaluation

2. Which of the following is a common metric used to evaluate the retrieval part of a RAG system?

easy

A. Mean squared error

B. BLEU score

C. Cross-entropy loss

D. Retrieval precision

Solution

1. Step 1: Identify retrieval metrics

   Retrieval precision measures how many retrieved documents are relevant.

2. Step 2: Match metric to retrieval

   BLEU is for text generation, cross-entropy and MSE are loss functions, not retrieval metrics.

3. Final Answer:

   Retrieval precision -> Option D

4. Quick Check:

   Retrieval metric = precision [OK]

Hint: Precision measures retrieval relevance, not BLEU or loss [OK]

Common Mistakes:

• Choosing BLEU which is for generation
• Confusing loss functions with evaluation metrics
• Ignoring retrieval-specific metrics

3. Consider this Python snippet evaluating a RAG model's answer quality using F1 score:

from sklearn.metrics import f1_score
true_answers = ["cat", "dog", "bird"]
pred_answers = ["cat", "dog", "cat"]
f1 = f1_score(true_answers, pred_answers, average='macro')
print(round(f1, 2))

What will be the output?

medium

A. Error due to string inputs

B. 0.75

C. 0.56

D. 1.00

Solution

1. Step 1: Verify f1_score handles strings

   sklearn's f1_score supports string labels directly via internal encoding.

2. Step 2: Compute macro F1

   Classes: 'bird', 'cat', 'dog'
   • 'bird': F1 = 0 (TP=0, predicted 0 times)
   • 'cat': prec=1/2=0.5, rec=1/1=1, F1=2×0.5×1/(0.5+1)=0.67
   • 'dog': F1=1
   Macro F1 = (0 + 0.67 + 1)/3 ≈ 0.5556, round(0.56, 2) = 0.56

3. Final Answer:

   0.56 -> Option C

4. Quick Check:

   macro F1 = (0 + 0.67 + 1)/3 = 0.56 [OK]

Hint: f1_score works on strings; macro F1=(0+0.67+1)/3=0.56 [OK]

Common Mistakes:

• Computing micro F1 or accuracy (0.67)
• Expecting error due to strings
• Wrong per-class calculation (0.75)

4. You have this code snippet to compute retrieval precision but it gives wrong results:

retrieved_docs = ["doc1", "doc2", "doc3"]
relevant_docs = ["doc2", "doc4"]
precision = len(set(retrieved_docs) & set(relevant_docs)) / len(relevant_docs)
print(round(precision, 2))

What is the bug and how to fix it?

medium

A. Divide by len(retrieved_docs) instead of len(relevant_docs)

B. Use union instead of intersection in numerator

C. Convert lists to tuples before set operations

D. No bug, code is correct

Solution

1. Step 1: Understand precision formula

   Precision = relevant retrieved / total retrieved, so denominator must be retrieved docs count.

2. Step 2: Identify denominator mistake

   Code divides by len(relevant_docs), which is recall formula denominator.

3. Step 3: Fix denominator

   Change denominator to len(retrieved_docs) to compute precision correctly.

4. Final Answer:

   Divide by len(retrieved_docs) instead of len(relevant_docs) -> Option A

5. Quick Check:

   Precision denominator = retrieved docs count [OK]

Hint: Precision divides by retrieved docs count, not relevant docs [OK]

Common Mistakes:

• Mixing precision with recall formula
• Using union instead of intersection
• Ignoring set conversion issues

5. You want to evaluate a RAG model combining answer F1 score and retrieval precision into a single metric. Which approach is best to fairly combine these metrics?

hard

A. Add F1 score and retrieval precision directly

B. Calculate the harmonic mean of F1 score and retrieval precision

C. Use only the higher of the two scores

D. Multiply F1 score by retrieval precision without normalization

Solution

1. Step 1: Understand metric combination needs

   Combining metrics requires balancing both scores fairly, avoiding dominance by one.

2. Step 2: Evaluate combination methods

   Harmonic mean balances low and high values well; addition or multiplication can skew results.

3. Step 3: Choose harmonic mean

   Harmonic mean is common for combining precision and recall, so it suits combining F1 and retrieval precision.

4. Final Answer:

   Calculate the harmonic mean of F1 score and retrieval precision -> Option B

5. Quick Check:

   Harmonic mean balances combined metrics [OK]

Hint: Use harmonic mean to balance combined metrics fairly [OK]

Common Mistakes:

• Adding metrics without normalization
• Ignoring metric scale differences
• Choosing max score only