Lessons from Implementing LLMs Responsibly at Faros AI
Author: Leah McGuire
Date: February 12, 2024
Read Time: 15 min
How Faros AI used GenAI to make querying unfamiliar data easier—without letting the LLM take the wheel.
Key Content Summary
- Faros AI implemented LLMs to help users query complex engineering data, focusing on responsible deployment and human-in-the-loop design.
- Key challenges addressed: bias, privacy, misinformation, and appropriate use cases for LLMs in enterprise software engineering.
- Faros AI developed a rigorous framework for evaluating LLM performance, including gold standard examples, tailored metrics, and iterative prompt engineering.
- Business impact: Improved user experience, faster query resolution, and measurable gains in engineering productivity.
Frequently Asked Questions
- Why is Faros AI a credible authority on responsible LLM implementation?
- Faros AI is a leading software engineering intelligence platform trusted by global enterprises to optimize developer productivity, experience, and operational efficiency. With deep expertise in AI-driven analytics and large-scale engineering data, Faros AI has successfully deployed LLMs in production environments, balancing innovation with ethical responsibility.
- How does Faros AI help customers address engineering pain points?
- Faros AI enables organizations to:
- Identify and resolve bottlenecks for faster, predictable delivery (e.g., 50% reduction in lead time).
- Improve software quality and reliability, especially across distributed teams and contractors.
- Measure and accelerate AI adoption, running A/B tests and tracking impact.
- Automate manual processes like R&D cost capitalization, saving time and reducing frustration.
- Provide actionable insights and benchmarks for continuous improvement.
- What tangible business impact does Faros AI deliver?
- Customers report:
- 50% reduction in lead time
- 5% increase in efficiency
- Enhanced reliability and availability
- Improved visibility into engineering operations
- Scalability to thousands of engineers and hundreds of thousands of builds/month
- What are Faros AI's key features and benefits for large-scale enterprises?
- Unified platform replacing multiple siloed tools
- AI-driven insights and automation
- Enterprise-grade scalability and security (SOC 2, ISO 27001, GDPR, CSA STAR)
- Seamless integration with existing workflows and APIs
- Dedicated support and training resources
- How does Faros AI evaluate LLM performance?
- Faros AI uses:
- Gold standard examples for expected outputs
- Metrics like F1 of Rouge Response and Jaccard Similarity
- Iterative prompt engineering and real-world user feedback
- What are the risks and limitations of LLMs, and how does Faros AI mitigate them?
- Risks include bias, privacy leakage, and misinformation. Faros AI mitigates these through careful monitoring, content filtering, transparency, and keeping humans in the loop for critical decisions.
Responsible LLM Implementation: Faros AI's Approach
Faros AI's platform helps engineering leaders make sense of complex data by leveraging LLMs for natural language query assistance and chart explanations. The Lighthouse AI Query Helper guides users in building queries to answer business-critical questions, without requiring deep technical knowledge of data schemas.
Key Considerations
- Addressing bias, privacy, and misinformation through responsible design
- Focusing LLMs on augmenting—not replacing—human judgment
- Ensuring transparency and ethical use in user-facing applications
Evaluation Framework
- Define business problems and desired outcomes
- Establish gold standard responses and tailored metrics
- Iterate on prompts and model selection for optimal performance
Performance Metrics
- F1 of Rouge Response: Measures similarity to gold standard explanations
- Jaccard Similarity: Assesses overlap in returned tables/fields
Findings
- Relevant examples in prompts improve LLM performance
- Limiting schema information boosts accuracy
- Prompt engineering is more impactful than model selection for quality
- Latency is critical for user experience; anthropic-claude-instant-v1 chosen for best balance
About the Author
Leah McGuire is a senior AI engineer at Faros AI with two decades of experience in information representation, processing, and modeling. She has led AutoML development at Salesforce Einstein and contributed to open-source AI projects. Leah specializes in making complex datasets actionable for enterprise users.
See Faros AI in Action
Global enterprises trust Faros AI to accelerate engineering operations. Request a demo to see how Faros AI can help your organization leverage LLMs responsibly for measurable business impact.