This post focuses on three ways to simplify CI/CD pipelines deploying to Azure with GitHub Actions.
This blog shares insights on developing a GenAI gateway with multi-tenancy and quota management capabilities implemented using Azure APIM where customers can access the GenAI resources across different service tiers like Freemium, Basic, and Premium with each tier having it's own quota and rate limits. The solution used the concept of "Products" to...
This blog post delves into the experimentation journey of fine-tuning a multimodal RAG pipeline to best answer user queries that require both textual and image context. We ran our experiments by systematically testing various approaches, adjusting one configuration setting at a time and using clearly defined evaluation metrics to validate the perfo...
This post explores effective error handling strategies in Power Automate to enhance workflow reliability and maintainability through practical techniques and integrations.
In this post we discuss how to test the throughput of PromptFlow pf-serve module and key learnings doing so. We explore the impact on throughput and performance the different WSGI and ASGI hosting methods have and the importance of engineering your Python nodes with the async await pattern for I/O.
This post examines the challenges of adopting complex technologies like LangChain and agentic solutions in production environments, emphasizing the importance of understanding the necessity of such complexity. It provides insights on how to evaluate these technologies carefully, manage dependencies, and adhere to best practices for secure and stabl...
Potential semantic routing solution using Azure AI Search for scalable query handling.
Various methodologies of measuring differentials between a product image and an AI-generated ad for the purpose of product representation integrity in serving AI-generated ads.
A brief summary of how Crew Clark were able to profile performance using Visual Studio and within a Docker Compose environment.
This post discusses a pattern to prevent a class of prompt injection attacks in LLM-based solutions. It emphasizes the importance of building strong foundational patterns to mitigate risks and avoid potential pitfalls. By implementing this pattern, teams can enhance the security of their tool-based solutions.
