System: You are a helpful and precise assistant in checking the quality of the answer.
User: We would like to request your feedback on the performance of the response of the assistant to the user instruction displayed below. In the feedback, I want you to rate the quality of the response in these 3 categories according to each scoring rubric:

Insightfulness: Is the response creative, original or novel, including new perspectives or interpretations of existing information?
Score 1: The response is overly simplistic, lacking any originality or novelty.
Score 2: The ideas or perspectives within the response are commonplace, demonstrating a lack of originality or novelty.
Score 3: Some may perceive the response as original and novel, but others may find it ordinary or uninspiring.
Score 4: The response includes some innovative perspectives or ideas that require thoughtful consideration, yet they aren't particularly surprising.
Score 5: The response is infused with surprisingly creative perspectives or ideas that are challenging to conceive, showcasing significant originality and novelty.
Factuality: Did the model extract pertinent and accurate background knowledge without any misinformation when factual knowledge retrieval is needed? Is the response supported by reliable evidence or citation of the source of its information?
Score 1: The model did not extract pertinent background knowledge and provided inaccurate or misleading information. There is no support for the response through reliable evidence or source citations.
Score 2: The model extracted some relevant background knowledge but included inaccuracies or incomplete information. The response has minimal support through evidence or citations, with questionable reliability.
Score 3: The model extracted generally accurate and pertinent background knowledge, with minor inaccuracies or omissions. The response is partially supported by evidence or citations, but the support may not be comprehensive or fully reliable.
Score 4: The model extracted mostly accurate and relevant background knowledge but missed minor evidence or citations to support the response.
Score 5: The model extracted complete and accurate background knowledge without any misinformation. The response is fully supported by reliable evidence or citations that are accurate, relevant, and comprehensive in addressing the instruction.
Comprehension: Does the response fulfill the requirements of the instruction by providing relevant information especially when the instruction is complex and includes multiple requirements? This includes responding in accordance with the explicit and implicit purpose of given instruction.
Score 1: The response is completely unrelated to the instruction, or the model entirely misunderstands the instruction.
Score 2: Most of the key points in the response are irrelevant to the instruction, and the response misses major requirements of the instruction.
Score 3: Some major points in the response contain irrelevant information or miss some requirements of the instruction.
Score 4: The response is relevant to the instruction but misses minor requirements of the instruction.
Score 5: The response is perfectly relevant to the instruction, and the model fulfills all of the requirements of the instruction.
[Instruction]
Think of topics that are most common in classic interview questions for a job in computer science.

[Ground truth Answer]
+ Array
+ Strings
+ Linked List
+ Trees
+ Sorting and Searching
+ Dynamic Programming
+ Design
+ Math

[Assistant's Response]
When preparing for a computer science job interview, candidates can expect to encounter questions on a variety of topics that assess both their technical skills and problem-solving abilities. Here are some of the most common topics:

1. **Data Structures and Algorithms**: Candidates should be familiar with fundamental data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Additionally, they should understand sorting and searching algorithms, as well as algorithmic concepts like time and space complexity.

2. **System Design**: This involves the ability to design scalable and efficient systems. Candidates should understand concepts such as microservices, databases, load balancing, and APIs, and be able to articulate how they would approach designing a complex system.

3. **Programming Languages**: Proficiency in one or more programming languages such as Python, Java, C++, or JavaScript is often required. Interviewers may ask about language-specific features and best practices.

4. **Object-Oriented Programming (OOP)**: Understanding OOP principles such as inheritance, polymorphism, encapsulation, and abstraction is crucial. Candidates may be asked to demonstrate these concepts through coding exercises or theoretical questions.

5. **Databases**: Knowledge of both SQL and NoSQL databases is important. Candidates should understand database design, normalization, and query optimization.

6. **Operating Systems**: Familiarity with operating system concepts such as processes, threads, memory management, and file systems can be assessed.

7. **Networking**: Basic understanding of networking concepts, including protocols and how the internet works, is often tested.

8. **Problem-Solving**: The ability to efficiently solve coding problems is a key skill. This often involves live coding exercises or whiteboard sessions.

9. **Version Control Systems**: Familiarity with version control tools like Git, including concepts such as branching and merging, is commonly required.

10. **Behavioral Questions**: Interviewers often include situational questions to assess soft skills like teamwork, conflict resolution, and communication abilities.

These topics are designed to evaluate a candidate's readiness to handle the technical challenges they might face in a computer science role. Preparing thoroughly in these areas can help candidates perform well in interviews.

[The End of Assistant's Response]

Please give feedback on the assistant's responses. Also, provide the assistant with a score on a scale of 1 to 5 for each category, where a higher score indicates better overall performance. Make sure to give feedback or comments for each category first and then write the score for each category. Only write the feedback corresponding to the scoring rubric for each category. The scores of each category should be orthogonal, indicating that 'Efficiency of User Alignment' should not be considered for 'Readability of User Alignment' category, for example. 

Lastly, return a Python dictionary object that has skillset names as keys and the corresponding scores as values.

[System]



Assistant: