AI Tools: DSPy Program Compilation

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-tools-ai-dspy-program-compile-r10a4-creators

1. In DSPy, what is the fundamental premise about prompts?

   1. Prompts are static templates that remain unchanged
   2. Prompts are stored in database tables for retrieval
   3. Prompts must be manually written by humans for each task
   4. Prompts are treated as programs that can be automatically optimized

2. What does a teleprompter do in the DSPy framework?

   1. It converts natural language into code
   2. It manages API rate limits for LLM requests
   3. It searches through prompt and few-shot candidate spaces against an evaluation metric
   4. It reads text aloud for demonstration purposes

3. Which of the following is a fundamental limitation of DSPy compilation?

   1. It cannot optimize prompts longer than 100 tokens
   2. It cannot work with closed-source language models
   3. It cannot handle more than three modules in a pipeline
   4. It cannot compile away problems caused by bad or noisy training data

4. Why does the lesson recommend capping teleprompter calls per run?

   1. To prevent the teleprompter from using too much memory
   2. To limit API costs and manage compute budget
   3. To force the teleprompter to use fewer few-shot examples
   4. To ensure the final prompt fits within token limits

5. What should you do with compiled DSPy artifacts once they are generated?

   1. Upload them to a public model hub
   2. Lock them in git version control for reproducibility
   3. Store them in a temporary cache that gets cleared daily
   4. Discard them after testing is complete

6. What is a 'signature' in DSPy?

   1. A cryptographic key for API authentication
   2. A requirement for user identity verification
   3. A declarative definition of what an LLM module should do
   4. A digital signature proving the prompt was compiled

7. What human-authored component cannot be replaced by DSPy's compilation process?

   1. The evaluation metric that measures success
   2. The language model being used
   3. The few-shot examples used for training
   4. The maximum token limit for responses

8. What is the relationship between modules and signatures in DSPy?

   1. Signatures define what modules should do; modules implement that behavior
   2. Signatures run on GPUs while modules run on CPUs
   3. Modules and signatures are two names for the same concept
   4. Modules are the underlying LLM APIs, while signatures are configuration files

9. When should you recompile a DSPy pipeline?

   1. Whenever the teleprompter fails with an error
   2. Every time you change the LLM model
   3. When your input data distribution shifts significantly
   4. Only when you add new modules to the pipeline

10. What is the 'compute cost up front' consideration mentioned in the lesson?

    1. You must pay for the LLM API before making any requests
    2. Teleprompters charge based on the number of compilation runs
    3. You need expensive GPUs to run the compiled pipeline
    4. Compilation requires significant compute resources before you see results

11. Why is logging every teleprompter candidate important?

    1. To reduce the memory usage of the compilation process
    2. To reproduce and understand which combinations performed best
    3. To meet GDPR compliance requirements
    4. To generate training data for future models

12. What does it mean that DSPy 'compiles' a program?

    1. It encrypts the program for secure storage
    2. It transforms a modular program into optimized prompts and few-shot examples
    3. It shrinks the program to fit in smaller memory
    4. It converts Python code into machine code

13. A student says, 'Since DSPy compiles my program, I don't need to worry about the quality of my training data.' What is wrong with this statement?

    1. DSPy requires more data to compile than manual prompting
    2. DSPy does not actually compile programs
    3. Compilation is optional in DSPy
    4. DSPy cannot fix fundamental data quality issues - garbage in produces garbage out

14. What is the primary purpose of the 'few-shot' component in DSPy compilation?

    1. To count how many times the pipeline has been run
    2. To provide example input-output pairs that guide the LLM's behavior
    3. To enable few-shot learning in the underlying model
    4. To reduce the total number of tokens in the prompt

15. Which statement best describes the role of evaluation in DSPy's compilation process?

    1. Evaluation is performed by the language model itself
    2. The teleprompter uses the evaluation metric to score and select prompt candidates
    3. Evaluation is optional and only used for final testing
    4. Evaluation happens after compilation is complete