RAG Framework Selection: LangChain, LlamaIndex, Custom

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-tools-AI-RAG-frameworks-creators

1. A development team is building their first RAG prototype and wants to learn common RAG patterns quickly. Which approach best aligns with the lesson's guidance?

   1. Skip RAG entirely and use fine-tuned models for all tasks
   2. Use a dedicated RAG framework like LangChain or LlamaIndex to accelerate learning
   3. Use only open-source embedding models without any orchestration layer
   4. Build a custom implementation from scratch to avoid framework constraints

2. A senior engineering team has very specific data preprocessing requirements that don't fit standard framework abstractions. What does the lesson recommend?

   1. Build custom abstractions where framework abstractions don't fit
   2. Force the data to fit the framework's expected format
   3. Use the framework but implement workarounds that violate best practices
   4. Abandon RAG and use a different architecture

3. A team is evaluating whether to migrate from a framework to a custom RAG implementation in production. What uncertainty does the lesson highlight about this decision?

   1. Migration should always happen immediately for cost savings
   2. Custom implementations never require migration back to frameworks
   3. It is impossible to perfectly predict when migration will become necessary
   4. Migration is only needed for security compliance reasons

4. Which three factors does the lesson identify as inputs for deciding a RAG approach?

   1. Project stage, team capacity, and customization needs
   2. Budget, programming language, and cloud provider
   3. Model size, token limit, and embedding dimension
   4. Data volume, latency requirements, and user interface

5. What does the lesson identify as an unavoidable reality regardless of whether a team uses frameworks or custom code for RAG?

   1. RAG systems require zero maintenance
   2. Custom implementations are always cheaper
   3. Some operational burden will always exist
   4. Frameworks guarantee bug-free systems

6. Which RAG frameworks does the lesson specifically mention in its comparison?

   1. TensorFlow, PyTorch, and JAX
   2. LangChain, LlamaIndex, and Haystack
   3. React, Vue, and Angular
   4. SQL, NoSQL, and NewSQL

7. Why might a team choose to maintain familiarity with both framework tools and underlying primitives?

   1. Because primitives are always faster than frameworks
   2. To have flexibility between abstraction convenience and custom control
   3. To avoid ever learning a new programming language
   4. Because frameworks cannot be used in production

8. What is the 'build vs buy' economic consideration the lesson mentions for RAG implementations?

   1. Weighing the cost of framework constraints against the cost of building custom solutions
   2. Buying pre-trained models replaces the need for RAG entirely
   3. Building from scratch is always cheaper than using any framework
   4. Buying more powerful GPUs eliminates the need for RAG

9. A startup with a small engineering team and aggressive timeline needs to implement RAG. Based on the lesson, which approach would likely serve them best initially?

   1. Build a custom implementation to avoid framework licensing fees
   2. Skip RAG and use pure fine-tuning for all use cases
   3. Implement RAG manually using only low-level APIs
   4. Use a framework to accelerate development while meeting their timeline

10. What does the lesson identify as a common trajectory for RAG systems as they mature?

    1. Systems should always remain on frameworks to ensure stability
    2. Mature systems typically abandon RAG in favor of pure generation
    3. Custom implementations are only for initial prototypes
    4. Systems often evolve from framework-based prototypes toward custom or hybrid production approaches

11. When evaluating RAG frameworks for potential production use, what should teams consider regarding team fit?

    1. Whether the framework name is easy to pronounce
    2. Whether the team's existing skills align with the framework's programming model and documentation quality
    3. Whether the framework has the most attractive logo
    4. Whether the framework was created by a well-known company

12. What is an 'escape hatch' in the context of RAG framework selection?

    1. A method to exit the programming language used by the framework
    2. A deprecated feature that should not be used
    3. A security vulnerability that allows unauthorized access
    4. A way to bypass the framework's abstraction and access lower-level functionality when needed

13. A team has been using LlamaIndex in production but needs functionality that requires bypassing core framework abstractions frequently. What does the lesson suggest they consider?

    1. That they need to hire more developers immediately
    2. That LlamaIndex is unsuitable for all production use cases
    3. Whether their use case has outgrown the framework and warrants custom abstractions
    4. That they should never use escape hatches

14. What output does the lesson's decision framework recommend producing?

    1. A budget estimate for the next fiscal year
    2. A final decision that cannot be changed
    3. A list of all possible RAG frameworks
    4. A recommended approach with rationale and potential migration path

15. Why might a team choose a hybrid approach combining framework and custom components?

    1. Because frameworks prohibit customization entirely
    2. Because hybrid approaches have no additional complexity
    3. To leverage framework convenience for common patterns while adding custom code for specific requirements
    4. Because hybrid is always the cheapest option