How to Evaluate AI Answers Before Launch
Pre-release testing evaluates a system against a prepared task set. It does not inspect or guarantee every future answer.
Author
Michael Goldman
Sprinklenet Research contributor focused on retrieval quality, model behavior, prompt risk, and audit controls for enterprise AI systems.
Measuring AI Value Beyond Token Savings
Measure the cost of completing the work, including review, corrections, integration, support, and model usage.
Knowledge Spaces vs. Traditional Document Portals
Many document platforms now offer AI assistants. Evaluate whether the existing platform meets the task before adding another layer.
Data Pipeline Priorities Before Launching Retrieval
Before building the index, select a representative document set and test extraction, source identifiers, access rules, updates, and removals.
Vendor Due Diligence for AI Implementation Partners
Due diligence should test architecture, security thinking, production experience, and evidence of delivery.
Building AI Systems That Can Cite Their Sources
A citation should let the reader inspect the passage that supports a claim.
Retrieval Evaluation: What to Measure Before Launch
Separate retrieval failures from answer failures.
When to Use Fine-Tuning Instead of Retrieval
Fine-tuning teaches a model how to behave, not what is true.
How to Choose Between Knowledge Graphs and Vector Search
Embeddings measure how alike two pieces of text are; a graph records that two things are related, how, and on whose authority.
News
Knowledge Spaces Developer APIs Are Open
Sprinklenet has opened version 1.0 of the Knowledge Spaces developer APIs for teams building governed, multi-tenant AI workflows with controlled retrieval, configuration, and usage…
Building a Retrieval Layer for Operational Knowledge
Most operational AI failures trace back to retrieval, not the model: the wrong version wins, permissions leak, and withdrawn documents keep getting cited.
What AI Vendors Miss About Data Preparation
In Sprinklenet’s project experience, 60–80% of the effort goes into data preparation.
Sprinklenet AI Newsletter
Most weeks I send one short read on enterprise AI: what’s working in real deployments, what’s not, and what I’d do in your seat.
Jamie Thompson, Founder and CEO