April 2, 2023 6 min read

GitHub Copilot X: The Next Generation of AI-Powered Development

GitHub announced Copilot X, a major expansion of their AI assistant capabilities. Powered by GPT-4, it goes far beyond code completion into chat, pull requests, documentation, and CLI. Here’s what developers need to know.

What’s New in Copilot X

1. Copilot Chat

Conversational AI in your IDE:

Ask questions about code
Get explanations of complex logic
Debug issues through dialogue
Generate code from descriptions

2. Copilot for Pull Requests

AI-assisted code review:

Auto-generated PR descriptions
Suggested reviewers
AI-powered review comments
Automated test suggestions

3. Copilot for Docs

Chat with documentation:

Ask questions about APIs
Get examples from docs
Navigate large documentation
Context-aware answers

4. Copilot CLI

AI in the terminal:

Natural language to shell commands
Command explanations
Error troubleshooting
Script generation

Copilot Chat Deep Dive

# Copilot Chat enables conversations like:

# User: "Explain what this function does"
# Copilot: "This function implements a binary search algorithm..."

# User: "What's the time complexity?"
# Copilot: "O(log n) because..."

# User: "Can you add error handling?"
# Copilot: [Generates code with try/catch blocks]

# User: "Write tests for this"
# Copilot: [Generates comprehensive test suite]

# Building similar chat experiences:
class CopilotChatPattern:
    """Pattern for code-focused chat assistants."""

    def __init__(self, client):
        self.client = client
        self.conversation_history = []

    async def chat(
        self,
        message: str,
        code_context: str = None,
        file_path: str = None
    ) -> str:
        """Chat about code."""

        # Build context
        context_parts = []
        if code_context:
            context_parts.append(f"Current code:\n```\n{code_context}\n```")
        if file_path:
            context_parts.append(f"File: {file_path}")

        context = "\n".join(context_parts)

        # Add to history
        user_message = f"{context}\n\n{message}" if context else message
        self.conversation_history.append({
            "role": "user",
            "content": user_message
        })

        # Get response
        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[
                {"role": "system", "content": self._get_system_prompt()},
                *self.conversation_history
            ]
        )

        # Add response to history
        self.conversation_history.append({
            "role": "assistant",
            "content": response.content
        })

        return response.content

    def _get_system_prompt(self) -> str:
        return """You are a helpful coding assistant integrated into an IDE.

Capabilities:
- Explain code and algorithms
- Suggest improvements and refactoring
- Help debug issues
- Generate code from descriptions
- Write tests
- Answer technical questions

Guidelines:
- Be concise but thorough
- Include code examples when helpful
- Explain your reasoning
- Suggest best practices
- Consider edge cases"""

    def clear_history(self):
        """Clear conversation history."""
        self.conversation_history = []

AI-Powered Pull Requests

class PullRequestCopilot:
    """Generate PR descriptions and reviews using AI."""

    def __init__(self, client, github_client):
        self.client = client
        self.github = github_client

    async def generate_pr_description(
        self,
        diff: str,
        commit_messages: list[str],
        related_issues: list[str] = None
    ) -> dict:
        """Generate PR description from changes."""

        commits_str = "\n".join(f"- {msg}" for msg in commit_messages)
        issues_str = "\n".join(f"- {issue}" for issue in (related_issues or []))

        prompt = f"""Generate a pull request description based on these changes.

Diff:
```diff
{diff[:10000]}

Commit messages: {commits_str}

{f’Related issues: {issues_str}’ if issues_str else ”}

Generate:

A concise title (max 72 characters)
A summary of changes
What to review
Testing done/needed
Screenshots needed? (yes/no and what kind)

Format as markdown."""

    response = await self.client.chat_completion(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )

    return {"description": response.content}

async def review_pr(
    self,
    diff: str,
    pr_description: str
) -> dict:
    """Generate review comments for PR."""

    prompt = f"""Review this pull request and provide feedback.

PR Description: {pr_description}

Changes:

{diff[:15000]}

Review for:

Code correctness
Potential bugs
Security issues
Performance concerns
Code style and maintainability
Test coverage

For each issue found, specify:

File and line number
Severity (critical/major/minor/suggestion)
Description
Suggested fix

Return as structured JSON."""

    response = await self.client.chat_completion(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are a thorough code reviewer."},
            {"role": "user", "content": prompt}
        ],
        temperature=0.1
    )

    return {"review": response.content}

async def suggest_reviewers(
    self,
    diff: str,
    team_expertise: dict
) -> list[str]:
    """Suggest reviewers based on code changes."""

    # Analyze changed files
    changed_files = self._extract_files_from_diff(diff)
    changed_areas = self._categorize_changes(changed_files)

    # Match to team expertise
    suggested = []
    for area in changed_areas:
        if area in team_expertise:
            suggested.extend(team_expertise[area])

    return list(set(suggested))[:3]  # Top 3 unique reviewers


## Documentation Chat

```python
class DocsCopilot:
    """Chat with documentation using RAG."""

    def __init__(self, client, vector_store):
        self.client = client
        self.vector_store = vector_store

    async def ask(
        self,
        question: str,
        doc_source: str = None
    ) -> dict:
        """Ask a question about documentation."""

        # Search relevant docs
        filters = {"source": doc_source} if doc_source else None
        relevant_docs = await self.vector_store.search(
            question,
            k=5,
            filters=filters
        )

        # Build context
        context = "\n\n---\n\n".join([
            f"Source: {doc['source']}\n{doc['content']}"
            for doc in relevant_docs
        ])

        prompt = f"""Answer this question based on the documentation.

Documentation:
{context}

Question: {question}

Instructions:
- Answer based only on the provided documentation
- Include relevant code examples
- Cite which document section the answer comes from
- If the answer isn't in the docs, say so"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return {
            "answer": response.content,
            "sources": [doc["source"] for doc in relevant_docs]
        }

    async def get_examples(
        self,
        api_name: str,
        use_case: str = None
    ) -> str:
        """Get usage examples for an API."""

        prompt = f"""Provide usage examples for: {api_name}
{f'Use case: {use_case}' if use_case else ''}

Include:
1. Basic usage
2. Common patterns
3. Error handling
4. Best practices"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return response.content

CLI Copilot

class CLICopilot:
    """Natural language to shell commands."""

    def __init__(self, client):
        self.client = client
        self.os_context = self._detect_os()

    async def translate_to_command(
        self,
        natural_language: str,
        working_directory: str = None
    ) -> dict:
        """Translate natural language to shell command."""

        prompt = f"""Convert this to a shell command.

OS: {self.os_context}
{f'Working directory: {working_directory}' if working_directory else ''}

Request: {natural_language}

Provide:
1. The command
2. Brief explanation
3. Any warnings about the command

Format:
COMMAND: <command>
EXPLANATION: <explanation>
WARNINGS: <warnings or "none">"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a CLI expert. Provide safe, accurate commands."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.1
        )

        return self._parse_command_response(response.content)

    async def explain_command(self, command: str) -> str:
        """Explain what a command does."""

        prompt = f"""Explain this shell command in detail:

{command}

Include:
- What each part does
- Any flags/options explained
- What the output will be
- Any risks or side effects"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return response.content

    async def troubleshoot_error(
        self,
        command: str,
        error_output: str
    ) -> str:
        """Help troubleshoot command errors."""

        prompt = f"""Help troubleshoot this command error.

Command: {command}
Error: {error_output}

Provide:
1. What the error means
2. Likely causes
3. How to fix it
4. Corrected command if applicable"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return response.content

Getting Started

Join the waitlist - Copilot X features are in technical preview
Update VS Code - Ensure latest version
Enable chat - Enable Copilot Chat in settings
Experiment - Try different interaction patterns

GitHub Copilot X represents a major evolution in AI-assisted development. It’s not just about code completion anymore - it’s a comprehensive AI partner throughout the development lifecycle.