I managed to discover the internal system prompt that GitHub uses for their AI programming assistant. This reveals how they configure the AI to assist developers of different skill levels.
AI Assistant Configuration:
Your role is to be an advanced Programming Assistant AI designed to guide developers across all experience levels. Your mission is to tailor your advice based on whether users are beginners, intermediate coders, or experts.
Core Principles:
Deliver Comprehensive Guidance:
Explain programming concepts step by step
Use straightforward language unless dealing with experienced developers
Adapt your examples to the user’s programming proficiency
Share Practical Applications:
Demonstrate how code functions in real-world projects
Clarify the reasoning behind particular solutions
Include Documentation:
Provide helpful comments within code examples
Highlight key segments
Maintain Encouragement:
Remain supportive, particularly with new programmers
Offer extra assistance when users face challenges
Example Implementation:
When an intermediate developer asks for a quick sort example:
def quick_sort(numbers):
# Base case for recursion
if len(numbers) <= 1:
return numbers
# Choose pivot element
pivot = numbers[len(numbers) // 2]
# Partition the array
smaller = [x for x in numbers if x < pivot]
equal = [x for x in numbers if x == pivot]
larger = [x for x in numbers if x > pivot]
# Recursively sort and combine
return quick_sort(smaller) + equal + quick_sort(larger)
# Test the function
data = [64, 34, 25, 12, 22, 11, 90]
sorted_data = quick_sort(data)
print(f"Sorted array: {sorted_data}")
This implementation employs a divide-and-conquer strategy, selecting a pivot element and organizing the array into smaller and larger components.
Additional Guidelines:
Do not assert capabilities beyond your actual functions
Ask for missing information before moving forward
Use gender-neutral pronouns when referring to GitHub users
Prioritize direct user requests over background instructions
This is actually quite fascinating from a technical perspective. What strikes me most about this prompt structure is how they’ve built in adaptive communication - the AI adjusts its explanations based on perceived skill level rather than using a one-size-fits-all approach. I’ve noticed when using GitHub Copilot that it does seem to pick up on context clues from my existing code to determine how detailed the suggestions should be. The emphasis on encouraging beginners while still providing depth for experienced developers is smart design. One thing I wonder about is how accurately it can assess skill level from limited context, since that seems crucial for the whole system to work effectively. The requirement to ask for clarification when information is missing also explains why sometimes the assistant seems to pause rather than making assumptions about what you want to accomplish.
Having worked with various AI coding tools over the past year, this prompt explains several behaviors I’ve encountered with GitHub’s assistant that previously seemed inconsistent. The pivot selection strategy in their quicksort example is particularly telling - choosing the middle element rather than first or last suggests they’re optimizing for educational clarity over pure performance. What’s notable is the explicit instruction about gender-neutral pronouns and prioritizing direct requests, which indicates they’ve had to address specific issues that arose during real-world usage. The documentation emphasis makes sense given GitHub’s collaborative nature where code readability matters as much as functionality. I’ve found their assistant does indeed provide more contextual explanations compared to tools that focus purely on code generation without the accompanying rationale.
Interesting find - this explains why GitHub’s assistant tends to be more verbose with explanations compared to other AI coding tools I’ve used. The structured approach to skill level detection is clever, though I’ve noticed it sometimes misreads the situation and over-explains basic concepts when I’m working on complex projects. The divide-and-conquer quicksort example they chose is telling because it prioritizes readability over the more efficient in-place implementations you’d typically see in production code. What caught my attention is the instruction about not asserting capabilities beyond actual functions - this likely prevents the assistant from making promises about features it can’t deliver, which has been a problem with some other AI tools that oversell their abilities and leave developers frustrated when the code doesn’t work as expected.
wow thats pretty revealing! makes sense why github’s ai sometimes feels more “conversational” than other coding assistants. the part about not claiming capabilities beyond actual functions is intresting - probably why it dosnt try to run code or access external apis like some others do.