AI Tool Haystack Pipeline Evaluation: Measuring End-to-End Quality

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-tools-haystack-pipeline-eval-r9a4-creators

1. What is the primary responsibility of humans when establishing a Haystack pipeline evaluation harness?

   1. Implementing the automated regression report system
   2. Generating sample queries and documents for testing
   3. Writing the code that generates retrieval and generation metrics
   4. Deciding which metric thresholds determine whether a pipeline is ready for release

2. A team builds a Haystack pipeline evaluation without investing in a labeled set. What is the most likely outcome?

   1. The evaluation will reward confident-sounding but factually incorrect outputs
   2. The AI will naturally discover and correct its errors
   3. The pipeline will automatically improve its accuracy over time
   4. Metrics will become more precise as the system runs

3. Which metric category includes measures like recall@k and MRR?

   1. Regression metrics
   2. Retrieval metrics
   3. Generation metrics
   4. Classification metrics

4. Which metric category includes measures like faithfulness and answer correctness?

   1. Ranking metrics
   2. Retrieval metrics
   3. Indexing metrics
   4. Generation metrics

5. What must be established BEFORE a team begins tuning a Haystack pipeline?

   1. A video recording of pipeline behavior
   2. A labeled set of query-document-answer triplets
   3. An automated threshold adjustment system
   4. A regression report template

6. What is the primary purpose of a sampling plan in pipeline evaluation?

   1. To systematically cover different query types and document classes in testing
   2. To randomly select which users will test the system
   3. To determine which metrics will be calculated automatically
   4. To choose which programming language to use for the harness

7. Why cannot AI replace human review for ambiguous answers in pipeline evaluation?

   1. Ambiguity requires judgment about intent, context, and domain expertise that AI cannot reliably evaluate
   2. AI systems are too slow to review answers in real-time
   3. AI review would make the evaluation system too expensive
   4. Human review is required for legal compliance in all jurisdictions

8. What does a high 'faithfulness' score indicate about a generated answer?

   1. The answer is factually correct according to external knowledge
   2. The answer was generated quickly without errors
   3. The answer contains no grammatical mistakes
   4. The answer is supported by the documents retrieved by the pipeline

9. A team wants to automate their release decision based on evaluation metrics. What is the critical prerequisite for automating this process?

   1. Implementing continuous integration
   2. Using a more sophisticated AI model
   3. Establishing specific threshold values for each metric
   4. Removing human reviewers entirely

10. Which statement best describes the division of labor between AI and humans in Haystack pipeline evaluation?

    1. Humans generate metrics; AI interprets them
    2. AI handles everything except writing the final report
    3. AI and humans alternate weeks on evaluation tasks
    4. AI generates metrics and drafts plans; humans make threshold and quality decisions

11. What is 'answer correctness' primarily measuring?

    1. Whether the answer matches ground truth for the query
    2. Whether the answer was retrieved from a trusted source
    3. Whether the answer uses proper grammar
    4. Whether the answer is longer than a minimum word count

12. What is the fundamental risk when a labeled set is missing from a pipeline evaluation?

    1. The pipeline will refuse to generate answers
    2. The metrics will all show perfect scores
    3. There is no ground truth to distinguish correct from incorrect outputs
    4. The evaluation will run too slowly to be useful

13. Why might a pipeline produce confident-sounding but incorrect answers if evaluation is poorly designed?

    1. There is no mechanism to penalize factual errors if they sound authoritative
    2. The confidence metric overrides all other considerations
    3. The evaluation rewards longer answers regardless of accuracy
    4. The pipeline is intentionally programmed to sound confident

14. What does MRR (Mean Reciprocal Rank) specifically measure in retrieval evaluation?

    1. The average rank position of the first relevant result across queries
    2. The consistency of retrieval across multiple runs
    3. The total number of relevant documents retrieved
    4. The percentage of queries that return any results

15. In the context of Haystack pipelines, what is a 'pipeline' primarily?

    1. A version control system for code
    2. A sequence of processing stages from query to generated answer
    3. A type of AI model architecture
    4. A physical tube that carries data between servers