Custom AI and LLMs
Fine-tune when justified
Only when retrieval and prompts are not enough.
ops.reckap.com/ai/fine-tune
When to fine-tune
RAG ok?Prompts ok?Justify FT
Rollback to base model stays on the runbook.
A justification memo, training set rules, and a rollback to the base model if quality drops.
Only when retrieval and prompts are not enough.
Problems we hear, outcomes we ship
Buyer problems
- Fine-tune is proposed before RAG and prompts are exhausted.
- No rollback plan if the tuned model gets worse.
Outcomes
- Written justification and dataset rules.
- Eval harness with rollback to base.
What is included
- Justification
- Dataset rules
- Eval harness
- Rollback
How this work is shaped
Same desk language as the parent lane - scoped to this package.
ops.reckap.com/ai/fine-tune
When to fine-tune
RAG ok?Prompts ok?Justify FT
Rollback to base model stays on the runbook.
How we deliver
Click a step - the desk below updates. That panel is a preview, not another step.
Step preview
01, Justify
ops.reckap.com/ai/discover
Discover, job map
Reckap AI desk| Job | Data source | Risk line |
|---|---|---|
| Ticket triage | Helpdesk API | No auto-close |
| Lead qualify | CRM + site chat | Human closes |
| Doc Q&A | Allowlisted PDFs | Cite or escalate |
Pilot pick: one job with a named owner and a fail check.
More in this lane
Other packages under Custom AI and LLMs.
FAQ
Next step
Ready to talk scope?
Bring the job, the systems you already run, and who owns the handoff. We write the rest.