A Redditor shared a massive system prompt called BidBuddy that turns an LLM into a procurement copilot, handling everything from supplier research to negotiation scenarios to final selection documentation. This isn’t a toy prompt. It’s a 2,500-word system instruction that walks through an entire purchasing workflow, and the structure behind it is worth studying even if you never buy anything more complex than office supplies. The original post got significant traction because procurement is one of those domains where AI assistance sounds obvious in theory but almost nobody has actually built it out properly.
What BidBuddy Actually Does
The prompt defines a procurement agent with three operating modes:
- Quick task mode for one-off outputs like RFQ emails or comparison tables
- Structuring mode for building out specific procurement stages
- End-to-end mode that walks through a full 9-step purchasing process from need definition to implementation planning
The nine steps cover need definition, market research, RFQ creation, proposal evaluation, risk assessment, negotiation preparation, supplier selection, contract review, and implementation planning. That’s the full purchasing lifecycle, not just the front end. The clever part is the incremental design. As a buyer uploads new proposals, revised offers, or supplier documents throughout the process, the agent updates its risk analysis, comparison tables, and negotiation strategy automatically. You’re not starting over each time a new document arrives. The model carries forward its prior analysis and revises it rather than treating each upload as a fresh conversation.
The Prompt Architecture Worth Stealing
Even if procurement isn’t your field, there are patterns here that apply to any complex agent prompt:
- Mandatory diagnosis before action. The prompt forces a structured intake before generating anything. It asks about business need, constraints, timeline, and existing suppliers. This prevents the classic problem of an AI jumping straight to output with half the context missing. Think of it as forcing the model to ask the questions a senior consultant would ask before touching a keyboard.
- Multi-dimensional classification. Every purchase gets rated on complexity, urgency, and market structure. This gives the model a framework for calibrating its responses instead of treating every request the same way. A commodity purchase gets handled differently than a sole-source critical component, and the prompt encodes that distinction explicitly.
- Explicit fact/assumption separation. The prompt requires distinguishing between confirmed facts, assumptions, and recommendations. This single rule eliminates a huge category of AI hallucination problems in business contexts. When the model is forced to label something as an assumption, it can’t quietly present speculation as established fact.
- Three negotiation scenarios. Conservative, target, and ambitious outcomes get defined for every negotiation. This forces the model to think in ranges rather than single-point predictions. It also makes the output more useful in practice, because real negotiations don’t move in straight lines and you need fallback positions prepared before the conversation starts.
- Dynamic update rules. Scattered throughout the prompt are instructions to re-evaluate previous analysis when new data arrives. This is how you make a long conversation stay coherent instead of forgetting earlier context. Most prompts don’t address this at all, which is why long threads with AI tools tend to drift and lose coherence after the first few exchanges.
📋 Use Cases Beyond Procurement
The template works for any domain where you need structured decision-making under uncertainty:
- Vendor selection for software tools or SaaS platforms
- Hiring decisions with multiple candidates and evaluation criteria
- Investment analysis comparing opportunities across risk dimensions
- Project proposals where you need to evaluate competing approaches
- Agency or freelancer vetting, where scope, pricing, and risk all need structured comparison before committing
Swap out the procurement terminology, keep the diagnosis-classify-analyze-negotiate-document skeleton, and you have a reusable agent framework. The hiring version alone is worth building: imagine a structured intake that forces you to define the actual need before you write a job description, then tracks candidate evaluations against consistent criteria across all conversations.
What Could Be Better
The prompt is thorough but heavy. At 2,500+ words of system instructions, it eats a meaningful chunk of context window before any conversation starts. Breaking it into modular sections that load on demand, activating the negotiation module only when proposals are uploaded, for example, would make it more efficient for API use where token costs matter. The risk analysis also uses simple Low/Medium/High ratings without weighting. Adding a severity-times-likelihood matrix would give more nuanced prioritization when multiple risks compete for attention. A supplier with a medium probability of delay on a mission-critical component is more dangerous than a high probability of a minor billing error, and the current rating system treats them the same.
🔑 Prompt of the Day
Here is the core diagnostic sequence you can adapt for any domain agent:
When the user describes a need, begin with a quick diagnosis. Ask direct and simple questions. After receiving answers: 1) Summarize the need clearly. 2) Identify missing information. 3) Classify the request across relevant dimensions. Briefly explain the reasoning behind each classification. Do not ask unnecessary questions, ask only what is needed to move the process forward.
That pattern alone, diagnose, summarize, classify, explain reasoning, will improve any agent prompt you build. It works because it forces the model to demonstrate its understanding before acting on it, which surfaces misunderstandings early instead of halfway through a long output you then have to discard.
🔗 Original post by u/Savage_Azzax on r/PromptEngineering
What domain-specific agents have you built? Drop your use case below: the best ones always come from people who actually do the work.
I built a procurement agent prompt for sourcing, supplier comparison, risk analysis, and negotiation — looking for feedback
by u/Savage_Azzax in PromptEngineering