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:

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.
Conciseness: Is the response presented in a concise manner for the reader without any unnecessary information?
Score 1: The response is highly redundant or contains a lot of unnecessary information, requiring a complete rewrite for optimal clarity and efficiency.
Score 2: The response lacks conciseness and needs a substantial rewrite for better optimization.
Score 3: The response is somewhat concise but includes unnecessary information, requiring some edits for improved optimization.
Score 4: The response is mostly concise but could benefit from minor edits for better optimization.
Score 5: The response is optimally concise and does not contain any unnecessary information, requiring no further optimization.
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.
[Instruction]
In this task, your job is to execute summarization, with the aim of refining the original lengthy response into a more concise version. Carefully read the provided question and corresponding long-form answer, then proceed to eliminate less significant sentences to construct a condensed and concise response. Remember that you should not change the contents of survived sentences, i.e., extractive summarization.\n\nQuestion: why green laser pointers cost only a few dollars more than red laser pointer but green self-leveling laser levels cost hundreds of dollars more than their red counterparts\n\nOriginal Answer: Not my answer but an answer I found on a forum from 2004.\n\n > In a 640nm red laser pointer, there\'s a red-emitting diode and a lens to collimate (focus) the beam. \n\n > In a 532nm green laser (pointer or larger size), there\'s a BIG infrared laser diode that generates laser light at 808nm, this is fired into a crystal containing the rare-earth element "neodymium". This crystal takes the 808nm infrared light and lases at 1064nm (yes, deeper in the infrared!). This 1064nm laser light comes out of the NdYV04 (neodymium yttrium vanadium oxide) crystal and is then shot into a second crystal (containing potassium, titanium,  & amp; phosphorus, usually called KTP) that doubles the frequency to 532nm - the bright green color you see. This light is then collimated (focused) by a lens and emerges out the laser\'s "business end". Just before the lens, there\'s a filter that removes any stray IR (infrared) rays from the pump diode and the neodymium crystal.\n\nBasically, with green diode laser pointers there are lots of itty bitty parts, and they all need to be aligned by hand. If the polarisation is "off", one or both crystals need to be turned. The overall process of making and the parts make the green one more expensive. With red diode lasers, you just slap in the diode and slap a lens in front of it, which makes it cheaper. \n\nYou can also see an image [here](_URL_0_) which more or less shows how the green laser pointer is more complex.

[Ground truth Answer]
Not my answer but an answer I found on a forum from 2004. > In a 640nm red laser pointer, there\'s a red-emitting diode and a lens to collimate (focus) the beam. > In a 532nm green laser (pointer or larger size), there\'s a BIG infrared laser diode that generates laser light at 808nm, this is fired into a crystal containing the rare-earth element "neodymium". This crystal takes the 808nm infrared light and lases at 1064nm (yes, deeper in the infrared!). This 1064nm laser light comes out of the NdYV04 (neodymium yttrium vanadium oxide) crystal and is then shot into a second crystal (containing potassium, titanium,  & amp; phosphorus, usually called KTP) that doubles the frequency to 532nm - the bright green color you see. This light is then collimated (focused) by a lens and emerges out the laser\'s "business end". Just before the lens, there\'s a filter that removes any stray IR (infrared) rays from the pump diode and the neodymium crystal. Basically, with green diode laser pointers there are lots of itty bitty parts, and they all need to be aligned by hand. If the polarisation is "off", one or both crystals need to be turned. The overall process of making and the parts make the green one more expensive. With red diode lasers, you just slap in the diode and slap a lens in front of it, which makes it cheaper. You can also see an image [here](_URL_0_) which more or less shows how the green laser pointer is more complex.

[Assistant's Response]
Green laser pointers are slightly more expensive than red ones due to their complex manufacturing process. A red laser pointer typically consists of a simple red-emitting diode and a lens, making it easy and inexpensive to produce. In contrast, a green laser pointer involves a more intricate assembly: it starts with an infrared laser diode that produces 808nm light, which is then converted to 1064nm light through a neodymium crystal. This light is further processed through a second crystal to achieve the 532nm green output. The alignment of multiple components in green lasers requires precision, contributing to their higher cost.

On the other hand, green self-leveling laser levels are significantly more expensive than red ones due to their advanced technology and features, which are designed for more professional and precise applications. This complexity in construction and functionality explains the price disparity between the two types of laser levels.

[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: