When you’re selling complex, high-value solutions, typically pre-sales teams spend a ridiculous amount of time answering variations of the same questions. Yes, customers want to know the details before they even consider moving forward. Configuration options, pricing structures, past implementations, compliance info.
What’s the challenge? This information is scattered across sales decks, internal wikis, spec sheets, and the minds of a few seasoned sales engineers who are drowning in demo requests.
Is there a better way to solve that? Let’s explore RAG – Retrieval-Augmented Generation.
What is RAG and why it can be interesting for pre-sales?
AI systems, specifically LLM’s – Large Language Models like chatGPT generate text by guessing the next likely word in a sentence. This is fine if you’re writing a marketing blurb based on open and public knowledge, but it’s risky when you need accurate, context-aware answers in a pre-sales process that will be grounded in your organisation knowledge (that is not public and is not “known” to LLMs).
RAG is a technique that might be helpful in such case. It’s a two-step process enabling to “fuel” LLMs with additional, private context. RAG consists of:
- Retrieval – Instead of relying solely on the AI’s pre-trained knowledge, it fetches relevant documents from a company’s internal knowledge base and appends it as an additional context in the the prompt
- Generation – The AI then synthesises a response based on the retrieved information, keeping it relevant and up-to-date.
For pre-sales teams, this means AI can respond to customer inquiries using information fetched from the private data sources, not just generic, public or outdated knowledge.
How to build RAG for pre-sales
If you want to build a RAG system that actually helps pre-sales teams, here’s what you need to think about:
1. Get Your Data in Order
Your AI assistant is only as good as the information it can retrieve. That means:
- Structuring product documentation, pricing sheets, and case studies in a searchable format.
- Attempting to capture the knowledge that is in people’s heads – digging insights out of Slack threads and buried PDFs.
- Filtering out outdated information to prevent false responses.
2. Set Up an Efficient Retrieval System
Not all documents are created equal. A good retrieval pipeline should:
- Use vector search (via tools like FAISS or Weaviate) to find semantically relevant documents, not just keyword matches.
- Implement filters (e.g., customer region, product version) to avoid pulling in irrelevant data.
- Rank sources by reliability—so a formal spec sheet outweighs a random internal email.
3. Fine-Tune the Generation Model
Once the AI retrieves relevant documents, it needs to generate an accurate and readable response. This means:
- Retrieval verification – If the LLM can’t find a solid source, it should say “I don’t know” rather than make something up.
- Response constraints – Some answers need to be precise, like pricing, while others can be more flexible, like best practices.
- Formatting – Delivering responses in a structured way (think bullet points, tables, or a concise summary) rather than long-winded AI monologues.
4. Deploy and Iterate
RAG is not a set-it-and-forget-it tool. Once you roll it out:
- Track which responses sales teams actually use (vs. ignore).
- Let reps flag inaccurate or incomplete answers to improve retrieval ranking.
- Continuously add new data sources as the business evolves.
Does it sound like a lot of hassle? Then maybe you should speak with dainamite team 🙂
We’ve already sorted it out for you.
The End Goal: a smarter, faster pre-sales process
A well-built RAG system won’t replace your pre-sales team. But it can definitely make them more effective. Instead of drowning in customer inquiries, RFIs, compliance questionnaires, they can focus on solving real customer challenges while AI helps them to speed up answering repeating questions.
Would you let AI take over parts of your pre-sales process, or do you think the human touch is still irreplaceable?
Want to get periodic insights about pre-sales?