Combining retrieval with agent reasoning in Agentic AI - Model Metrics & Evaluation

Metrics & Evaluation - Combining retrieval with agent reasoning

Which metric matters for this concept and WHY

When combining retrieval with agent reasoning, the key metrics are Precision, Recall, and F1 score. These metrics tell us how well the system finds the right information (retrieval) and uses it correctly to answer or act (reasoning). Precision shows how many retrieved items are actually useful, recall shows how many useful items were found, and F1 balances both. This helps us know if the agent is both accurate and thorough.

Confusion matrix or equivalent visualization (ASCII)

Confusion Matrix for Retrieval + Reasoning Output:

               Predicted Relevant   Predicted Irrelevant
Actual Relevant       TP (True Positive)      FN (False Negative)
Actual Irrelevant     FP (False Positive)     TN (True Negative)

Example numbers:
TP = 80, FP = 20, FN = 10, TN = 90
Total samples = 80 + 20 + 10 + 90 = 200

From this:
Precision = TP / (TP + FP) = 80 / (80 + 20) = 0.8
Recall = TP / (TP + FN) = 80 / (80 + 10) = 0.8889
F1 = 2 * (0.8 * 0.8889) / (0.8 + 0.8889) ≈ 0.842

Precision vs Recall tradeoff with concrete examples

Imagine the agent is a helper that finds documents and then reasons to answer questions.

• High Precision, Low Recall: The agent only returns very sure answers. It rarely makes mistakes but might miss some good answers. Good when wrong answers are costly, like medical advice.
• High Recall, Low Precision: The agent tries to find all possible answers, even if some are wrong. Good when missing any answer is bad, like searching for all fraud cases.

Balancing precision and recall depends on the task. F1 score helps find a good middle ground.

What "good" vs "bad" metric values look like for this use case

Good metrics: Precision and recall above 0.8 show the agent finds most relevant info and reasons well. F1 above 0.8 means balanced performance.

Bad metrics: Precision or recall below 0.5 means the agent either misses too much or makes many mistakes. F1 below 0.5 shows poor overall quality.

Example: Precision=0.9, Recall=0.85, F1=0.87 is good. Precision=0.4, Recall=0.7, F1=0.52 is bad.

Metrics pitfalls

• Accuracy paradox: If most data is irrelevant, a model that always says "irrelevant" can have high accuracy but no real skill.
• Data leakage: If retrieval uses future info, metrics look better but model won't work in real life.
• Overfitting: High training metrics but low test metrics mean the agent memorizes instead of reasoning.
• Ignoring reasoning errors: Good retrieval but poor reasoning can still give wrong answers, so measure both parts.

Self-check question

Your combined retrieval and reasoning agent has 98% accuracy but only 12% recall on relevant items. Is it good for production? Why or why not?

Answer: No, it is not good. The very low recall means the agent misses most relevant information, even if it is usually correct when it does find something. This can cause important answers to be lost, which is risky in real applications.

Key Result

Precision, recall, and F1 score best measure combined retrieval and reasoning quality by balancing correctness and completeness.

Practice

1. What is the main benefit of combining retrieval with agent reasoning in AI?

easy

A. It makes AI run faster without using any data.

B. It helps AI find and use information more accurately.

C. It allows AI to ignore facts and guess answers.

D. It reduces the AI's ability to explain its answers.

Solution

1. Step 1: Understand retrieval role

   Retrieval helps AI find relevant facts from data sources.

2. Step 2: Understand reasoning role

   Reasoning uses those facts to form thoughtful, accurate answers.

3. Final Answer:

   It helps AI find and use information more accurately. -> Option B

4. Quick Check:

   Combining retrieval and reasoning = better accuracy [OK]

Hint: Remember: retrieval finds facts, reasoning uses them [OK]

Common Mistakes:

• Thinking retrieval ignores data
• Believing reasoning guesses without facts
• Assuming combination slows AI
• Confusing retrieval with ignoring facts

2. Which code snippet correctly shows how an agent uses retrieval results for reasoning?

easy

A. facts = retriever.get_facts(query)
answer = reasoner.use(facts)

B. answer = reasoner.get_facts(query)
facts = retriever.use(answer)

C. retriever = reasoner.get_facts()
query = answer.use(facts)

D. facts = reasoner.get_facts()
answer = retriever.use(facts)

Solution

1. Step 1: Identify retrieval step

   Retriever should get facts first using the query.

2. Step 2: Identify reasoning step

   Reasoner uses those facts to produce the answer.

3. Final Answer:

   facts = retriever.get_facts(query)\nanswer = reasoner.use(facts) -> Option A

4. Quick Check:

   Retriever gets facts, reasoner uses facts [OK]

Hint: Retriever gets facts first, then reasoner uses them [OK]

Common Mistakes:

• Swapping roles of retriever and reasoner
• Calling reasoner before retrieval
• Using wrong method names
• Mixing variable assignments

3. Given this code, what will be the output?

facts = ['Paris is capital of France', 'France is in Europe']
answer = reasoner.use(facts)
print(answer)

Assuming reasoner.use() combines facts into a summary sentence.

medium

A. "Paris is capital of France and France is in Europe."

B. "Paris is capital of Europe."

C. "France is capital of Paris."

D. "Europe is in France."

Solution

1. Step 1: Understand input facts

   Facts list contains two true statements about Paris and France.

2. Step 2: Reasoner combines facts

   Reasoner merges facts into a combined sentence preserving meaning.

3. Final Answer:

   "Paris is capital of France and France is in Europe." -> Option A

4. Quick Check:

   Combined facts form correct summary [OK]

Hint: Look for combined true facts in output [OK]

Common Mistakes:

• Mixing up place names
• Ignoring fact order
• Assuming reasoner changes facts
• Choosing unrelated sentences

4. Identify the error in this code snippet combining retrieval and reasoning:

facts = reasoner.get_facts(query)
answer = retriever.use(facts)
print(answer)

medium

A. Variables facts and answer are swapped.

B. The print statement is missing parentheses.

C. Retriever should get facts, not reasoner.

D. No error; code runs correctly.

Solution

1. Step 1: Check roles of components

   Retriever is responsible for getting facts from query.

2. Step 2: Identify misuse

   Code wrongly calls reasoner.get_facts instead of retriever.get_facts.

3. Final Answer:

   Retriever should get facts, not reasoner. -> Option C

4. Quick Check:

   Retriever gets facts first [OK]

Hint: Retriever finds facts; reasoner uses them [OK]

Common Mistakes:

• Confusing retriever and reasoner roles
• Ignoring method names
• Assuming print syntax error
• Thinking variables are swapped

5. You want an AI agent to answer questions about a large document collection. Which approach best combines retrieval with reasoning to improve answer quality?

hard

A. Use only a reasoner without any retrieval step.

B. Use a reasoner to guess answers, then a retriever to check if facts exist.

C. Use only a retriever to return raw documents as answers.

D. Use a retriever to find relevant document parts, then a reasoner to synthesize an answer from those parts.

Solution

1. Step 1: Understand retrieval role

   Retriever finds relevant parts from large documents to reduce search space.

2. Step 2: Understand reasoning role

   Reasoner uses retrieved parts to create a clear, accurate answer.

3. Step 3: Evaluate options

   Use a retriever to find relevant document parts, then a reasoner to synthesize an answer from those parts. correctly sequences retrieval then reasoning for best quality.

4. Final Answer:

   Use a retriever to find relevant document parts, then a reasoner to synthesize an answer from those parts. -> Option D

5. Quick Check:

   Retrieve first, then reason [OK]

Hint: Retrieve relevant info first, then reason for answer [OK]

Common Mistakes:

• Reversing retrieval and reasoning order
• Skipping retrieval step
• Using only raw documents as answers
• Relying on guessing without facts