What Questions Should I Include in an RFP for Custom LLM Development?

A good RFP can save months of wasted time with the wrong vendors. A weak one produces glossy proposals that hide gaps in security, architecture, and real-world experience. When you create an RFP custom LLM development process, you need questions that go beyond marketing claims to reveal how a partner will build, secure, and operate AI systems in your environment.

You are not just buying code. You are selecting a partner for models, data, workflows, and governance that will sit inside your most critical processes.

• An effective RFP custom LLM development template asks about use case approach, architecture, data handling, security, and governance.
• You should require concrete examples, diagrams, and metrics, not only high-level promises.
• Questions must cover both build and run, including monitoring, evaluation, and incident response.
• Tailor sections to your risk profile and domains while keeping a consistent core.
• Codieshub helps organizations design and respond to RFPs for custom LLM development with enterprise-grade depth.

Before listing questions, be clear about what your RFP custom LLM development aims to uncover:

• Can the vendor solve your specific business problems, not just generic AI tasks?
• Do they understand enterprise integration, security, and governance?
• Have they delivered and operated similar systems at your scale and risk level?
• How do they think about cost, ongoing maintenance, and ownership?

Your questions make it easy to compare vendors on these points.

Ask vendors to show how they think, not just what tech they use.

Key questions:

• Which of our described use cases do you believe are of the highest value and why?
• How would you phase delivery, from discovery to pilot to full rollout?
• What metrics would you use to measure success for these use cases?
• Describe one similar project you have delivered, including outcomes, not just features.

This section tests whether they can translate your goals into a realistic plan.

Here, you probe their technical approach for RFP custom LLM development.

Key questions:

• Provide a high-level architecture diagram for the proposed solution, including models, retrieval, orchestration, and integrations.
• Which models or providers do you recommend and why? How easily can we change them later?
• How will you implement retrieval augmented generation over our data, and where will the indexes live?
• How do you handle multi-model strategies, such as using smaller models for simple tasks and larger ones for complex tasks?

You want to see a modular, flexible design rather than hard-wired dependencies.

Data is often the most sensitive part of any RFP custom LLM development.

Key questions:

• What data from our systems would your solution need to access, and how will it be stored and processed?
• How do you implement data minimization and redaction for prompts, logs, and training or fine-tuning datasets?
• Describe your approach to integrating with our core systems, such as CRM, ERP, ticketing, and document repositories.
• How do you support data residency, tenant isolation, and deletion requirements?

Look for clear, concrete answers tied to specific technologies and processes.

This section ensures the vendor can meet your baseline posture.

Key questions:

• Describe your security architecture for LLM-based solutions, including identity, access control, key management, and network segmentation.
• Which security and compliance certifications or attestations do you hold, for example, SOC 2, ISO 27001, or sector-specific frameworks?
• How do you implement guardrails and policy enforcement around model behavior, tool use, and data access?
• What is your process for reviewing and approving high-risk use cases?

These questions anchor the security part of your RFP custom LLM development.

Production AI must be observable and testable.

Key questions:

• How do you log prompts, outputs, and tool calls while protecting sensitive data?
• What metrics do you track for quality, safety, latency, and cost?
• Describe your approach to continuous evaluation, including sampling, human review, and A/B testing.
• How do you handle model versioning, rollbacks, and controlled rollouts?

You want evidence that they treat AI behavior as something to measure and manage, not trust blindly.

Ask how they design safe, usable workflows.

Key questions:

• How will users see, control, and override AI suggestions in the proposed solution?
• What patterns do you use to communicate limitations, confidence, or uncertainty to users?
• Describe your approach to gathering user feedback and using it to improve prompts, models, and UX.

This part of your RFP custom LLM development ensures the vendor thinks beyond APIs to real people.

Clarify how you will work together and what happens after launch.

Key questions:

• Describe your proposed team structure, roles, and collaboration model for this engagement.
• What is your typical timeline from discovery to first production release for similar projects?
• How do you handle ongoing support, incident response, and change requests after go-live?
• What components and IP will we own, and what remains proprietary to you?

Ownership and lifecycle clarity prevent surprises later.

Finally, bring cost and scale into the RFP custom LLM development conversation.

Key questions:

• Provide a breakdown of one-time implementation costs versus ongoing run and support costs.
• How do your fees change with volume, such as more users or higher API throughput?
• What strategies do you use to optimize cost over time, including caching, model tiering, or routing?
• Are there any dependencies on specific vendors or licenses we should be aware of?

You are looking for transparency and a credible path to sustainable economics.

• Mark some questions as mandatory, especially in security and data handling.
• Limit free-form questions to what you will actually read and compare.
• Ask for short, specific examples rather than broad essays.
• Use scoring rubrics per section to make evaluation consistent and fair.

A well-structured RFP custom LLM development package makes it easier for good vendors to respond and for you to compare them.

Codieshub helps you:

• Understand what enterprise buyers expect in an RFP custom LLM development and how to answer confidently.
• Package your architecture, security, and governance story in ways that satisfy procurement and security teams.
• Avoid over-committing to custom work that your current platform cannot support.

Codieshub works with your teams to:

• Design or refine your standard RFP custom LLM development template and scoring model.
• Review vendor responses for technical and governance depth, not just slide decks.
• Define reference architectures and guardrails that any selected partner must follow.

Draft your initial RFP custom LLM development using the sections above and tailor them to your domains and risk profile. Involve security, legal, and key business stakeholders in reviewing the questions. Use the first round of vendor responses to refine which questions are most discriminating, then standardize that template for future AI initiatives so every new partner is evaluated on the same clear, enterprise-grade criteria.

1. Should we reuse our generic software RFP for LLM projects?
Partly, but you will miss AI-specific issues around models, prompts, data usage, and evaluation. It is better to extend your standard RFP with focused sections for custom LLM development.

2. How many vendors should we invite to respond?
Often, three to six serious candidates are enough. More than that can create noise and slow the process without improving outcomes.

3. Do we need to specify the exact model in the RFP?
Not always. Describe your requirements and constraints, and let vendors propose models and architectures. You can then assess how they justify their choices.

4. How do we compare very different proposals?
Use a scoring rubric per section, including business fit, architecture, security, governance, and cost. Normalize scores and hold a review session with both technical and business stakeholders.

5. How does Codieshub help with RFPs for custom LLM development?
Codieshub brings templates, technical expertise, and enterprise experience to shape your RFP custom LLM development process and interpret vendor answers, so you choose partners who can deliver secure, reliable, and valuable AI systems in your environment.