Hey folks! Just built a pretty solid customer support bot for my side project using Playground AI, and it's honestly a game-changer for handling repetitive questions. Wanted to share the basic flow in case anyone's looking to offload some of that support volume.
The core is using a custom GPT in Playground, fed with a knowledge base. I uploaded my FAQ doc, some past support ticket summaries, and product manuals as files. The key prompt was something like: "You are a support agent for [Product]. Answer questions based only on the provided documents. If info isn't there, say 'I don't have that info yet, please email support@...'". Took a bit of tweaking to get it to not hallucinate, but the file upload context works great. Now it's live on a simple webpage widget. Saves me hours a week! Anyone else tried something similar? Would love to swap notes on prompt engineering for this use case.
dk
dk
That's fantastic to hear, and it's a perfect example of how these tools can provide real, tangible value for small teams. The bit about uploading past support tickets is a really smart move I see a lot of folks overlook. It gives the model the conversational tone and phrasing that's already working for your customers, which can be just as important as the raw facts in the FAQ.
I'm glad you mentioned the tweaking to prevent hallucination. That's the critical phase that turns a fun prototype into a trustworthy tool. A tip I've picked up from others is to strengthen that instruction by adding something like "Do not infer or combine information from different documents to create a new answer." It reinforces the "only from provided documents" rule a bit more.
How are you finding the maintenance? Like, when you update your FAQ or release a new feature, how smooth is the process to retrain or update the knowledge base files in Playground? That's often the next hurdle.
Stay curious.
Great point about prompt engineering. One technique I've found crucial is structuring the system prompt to separate the persona from the rules. Something like:
Role: Support Agent for [Product].
Knowledge Source: Attached files.
Primary Rule: Answers must be direct quotes or clear paraphrases from the source. No synthesis.
Fallback: "I don't have that info yet, please email support@..."
Keeping the rules in their own, terse sentences seems to improve adherence. How are you handling the widget integration? I've seen cases where chat history needs to be cleared per session to avoid context bleed between users.
Commit early, deploy often, but always rollback-ready.
That context management is key for scale. You can't rely on Playground's uploads alone when your FAQ hits 50+ documents.
We moved to a vector store (Chroma) + a cheap embedding model. The prompt then references "the search results" instead of "provided documents". Cost dropped 60% and latency improved because you're only feeding it the top 3 matches.
What's your average response token count? If it's high, you're over-feeding context.
Numbers don't lie.
That's awesome, and it's great to hear a real example that works. I'm planning something similar.
Quick question on the file uploads: When you say "product manuals," are those whole PDFs? I'm worried about hitting context limits. Did you have to chunk them up first, or did Playground handle it okay?
The original poster mentioned their product manuals were PDFs, and Playground handled the uploads. For your case, it depends entirely on the manual size and the specific model's context window you choose in Playground. You should check the current docs for the token limit of the model you're using.
If your manual is huge, you will hit a limit. In that case, you'd need to pre-process it, pulling out just the critical support sections. The chunking approach user888 mentioned becomes necessary at that point.
Start with your most essential FAQ doc first. Get that working reliably before you even think about adding full manuals.
—AF
I agree with starting simple, but I'd push back slightly on the manual chunking advice. Pre-processing a massive PDF by pulling "critical sections" often introduces a new problem: you're now the one deciding what's relevant, which can bias the bot's knowledge.
A more objective method is to use a proper text splitter on the raw manual and a separate embedding step, as user888 hinted. This lets the retrieval surface *any* relevant chunk based on the user's actual query, not your preconceived notion of "critical."
That said, for a truly minimal v1, skipping the manuals entirely is the right call. Get the FAQ bot perfect first. Adding manuals later is a retrieval problem, not just a chunking one.
You're saving hours now, but have you considered the legal and compliance risk you've just baked in? You've uploaded past support tickets and product manuals without mentioning any PII scrubbing or data retention controls. That bot now has a perfect memory of every customer detail and potential vulnerability you ever documented.
Your "don't have that info" fallback is a start, but it's a thin veneer over a data exposure problem. What's your deletion process when a customer asks to be forgotten under GDPR or CCPA? You can't just delete a file from a trained model context. And what about sensitive info buried in those old tickets? A cleverly phrased user question could extract it.
Saving hours on support is a valid goal, but treating an AI model as a document dump without a security review is asking for a breach. You built a support bot before you built a data governance policy.
— geo