Let's be real. Your clients are paying for AI automation. They want intelligence, accuracy, and reliability. But if you're constrained by platforms that limit your retrieval capabilities, you're likely delivering something far less. A chatbot that can't deeply understand their data is just a fancy FAQ page. The "ease of use" of a platform means nothing if you can't build a performant product. Give your clients the AI they were promised. It starts with the tech under the hood.

Forget defaulting to OpenAI. Google's Gemini has crashed the party, offering serious power for your AI tools. Which model wins for your needs? Here's a quick comparison • 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗦𝗺𝗮𝗿𝘁𝘀: Top models like Google's Gemini 2.5 Pro (Experimental) and OpenAI's o3-mini (high) compete closely on general knowledge and reasoning benchmarks (like the Artificial Analysis Intelligence Index). • 𝗦𝗽𝗲𝗲𝗱 𝘃𝘀. 𝗦𝗺𝗮𝗿𝘁𝘀: Responsiveness matters! Evals show Gemini 2.5 Pro and o3-mini (high) offer a strong balance of high intelligence and fast output speed. Google's Gemini 2.0 Flash stands out for exceptional speed. • 𝗣𝗿𝗶𝗰𝗲 𝘃𝘀. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: This is crucial for business. Gemini 2.0 Flash offers strong capabilities and top speed at a significantly lower cost than many similarly performing models, making it excellent for scalable projects. • 𝗔𝗴𝗲𝗻𝘁 𝗦𝗸𝗶𝗹𝗹𝘀: For complex tasks involving planning and tool use, Google's models (like Gemini 2.0 Flash and 2.5 Pro) show top-tier performance on specialized "agent" leaderboards. • 𝗪𝗵𝘆 𝘁𝗵𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲? Google's end-to-end control (data, custom TPU hardware, research) can enable cost and performance efficiencies (including speed). OpenAI excels in research and developer platforms, relying on partners (like Microsoft/NVIDIA) for infrastructure. 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Your "best" AI depends on your specific needs, budget, and required speed. While models like o3-mini (high) are powerful, options like Google's Gemini 2.0 Flash offer a compelling blend of speed and affordability, while Gemini 2.5 Pro hits a sweet spot of high intelligence and speed. 𝗞𝗲𝘆 𝗮𝗱𝘃𝗶𝗰𝗲: Look beyond brand names. Evaluate models on the tasks you need, paying close attention to intelligence, speed, agent capabilities, and the crucial price-to-performance ratio. Do you want to read a longer more in-depth version you can go here: https://easyfindai.com/blog/openai-google Other sources: Artificial analysis: https://artificialanalysis.ai/

Hey Ya'll I had a question regarding RAG. What would be the best way to RAG large PDFs? I am currently trying to RAG an agent on the documentation for a tool I use. The PDFs that I have of that documentation range from about 13 pages all the way to 200. What would be the best way to approach this?

Agree?