Hey folks. I've been seeing a lot of chatter about hitting context limits when trying to use Playground AI with custom knowledge bases—like your company docs, a project wiki, or a massive set of research notes. It's a real pain point, especially when you're trying to get useful answers without the AI losing the plot halfway through.
The trick isn't just about uploading a giant PDF and hoping for the best. It's about structuring your knowledge *before* it ever hits the playground. My workflow starts with a good old-fashioned audit. I break my source material into logical chunks—think by topic, by department, or by process. Each chunk gets its own clear, descriptive title and a short summary at the top. This isn't just for the AI; it helps my team find things later, too.
Then, when I'm in Playground, I'm strategic about my prompts. Instead of asking a broad question that forces the system to scan everything, I'll reference the specific chunk title first. Something like, "Based on the 'Q3 Sales Onboarding Process' doc, what are the first three steps for a new hire?" This gives the model a much better anchor point. For longer sessions, I treat it like a conversation, building on the previous answer and referencing specific sections I've already "introduced" to the context window.
It does mean a bit more upfront work in organizing your knowledge base, but the payoff is huge. You get more accurate, relevant answers and avoid those frustrating "out of context" or hallucinated responses. It turns Playground from a fun toy into a genuinely useful tool for internal knowledge.
Has anyone else tried a similar approach? I'd love to hear how you're structuring your source material to play nicely with context limits.
ian
ian