AI Engineering Best Practices for Startups
Join us for an invite-only session tailored for CTOs and technical leaders at early to mid-stage startups navigating the rapidly evolving landscape of AI-powered development.
In this session, we'll explore proven AI engineering patterns that can significantly accelerate your team's productivity while maintaining code quality and security standards. Drawing from our experience building Cline and working with teams embracing AI, we'll share best practices for multiplying your engineering output.
What We'll Cover:
LLM Interaction Workflows: A practical look at patterns like Plan & Act versus single-step generation and their impact on code quality.
Smarter Token & Context Management: Techniques to maximize effective context and minimize unhelpful model outputs (hallucinations), especially in complex, multi-file operations.
Model Selection Insights: A comparative analysis of model architectures (coding vs. reasoning), context length trade-offs, and optimal use cases for Claude, GPT, Gemini, and DeepSeek variants.
Context Persistence Strategies: Implementing checkpointing and state management for long-running AI development tasks.
Secure AI Implementation: Best practices for security and data governance, including using VPC endpoints for secure inference.
There will be plenty of time for Q&A to address your specific challenges. You'll leave with practical patterns for accelerating development across your team and a roadmap for implementing AI tools that align with your organization's technical goals.
We look forward to seeing you there!
Note: While this session is hosted by the Cline team, our focus will be on platform-agnostic best practices that work across the AI development ecosystem.