Contact Information
Australia
- March 2, 2025
Case Study – provider agnostic AI chat interface
A client wanted a “chatbot” to interface with all of the providers (Google, OpenAI, Perplexity, Anthropic, and image generation models) so they could easily compare
Basic comparison between LLM providers
I’ve created this table of information to assist me in remembering the features and restrictions of each provider.
My issue with all AI providers / models
After testing multiple AI platforms, I realized they all had deal-breaking limitations—short conversation memory, prompt manipulation, censorship, rate limits, and unreliable file processing. Many chunk documents, making full-document interactions impossible, and frequently “hallucinate” answers instead of admitting uncertainty. Worse, AI providers prioritize storage efficiency over retaining context, making deep discussions frustrating. I needed an AI that could analyze entire documents, retain long chat histories, and offer real control—so I built my own system. Now, I have an AI that actually works the way I need it to.
From “AI in 7 Minutes” to Reality: The Hidden Costs and Complexities of Production-Ready AI
The marketing hype around AI often oversells simplicity, leaving many to grapple with the hidden complexities and costs of effective implementation. As you dive into the world of AI, you may find yourself entangled in a web of interconnected services, each with its own price tag. From API costs to infrastructure overhead and the significant human time investment, the reality can be sobering. Discover strategies to minimize expenses while maintaining functionality, and learn the harsh truths behind the enticing promises of “AI in 26 minutes.” Prepare for a journey that requires solid planning and patience.
The Illusion of AI Knowledge: Making LLMs Work for You Without Overreliance
Unlock the potential of AI without starting from scratch! Discover how large language models (LLMs) like GPT-4 operate on statistical patterns rather than true knowledge. Learn why simply uploading documents isn’t enough and how Retrieval-Augmented Generation (RAG) can enhance your brainstorming sessions. Explore practical strategies like fine-tuning, embedding systems, and specialized prompt engineering to tailor AI responses to your unique business needs. Whether you’re looking to streamline workflows or enhance relevance, our insights will guide you in leveraging existing models effectively. Dive in to transform your approach to AI and maximize its utility for your organization!