Citation quality
Fact citation AI: build an evidence-grounded answer
Connect claims to passages before generation, then check that the final answer stays within the evidence.
03 FIELDNOTES / TOPIC
LLMs
A focused reading path through The Evidence Journal. Language-model workflows need evidence checks that are separate from fluency. These articles cover claim-level evaluation, evidence-grounded answer generation, and vendor-neutral assessment of AI fact-checking services.
How to use this collection
Start by defining the source collection and the task a model is allowed to complete. Then inspect whether the final wording is actually supported, whether important claims lack citations, and how difficult cases reach a reviewer. Compare systems on shared examples and keep the cost of a reviewed result visible alongside automated scores.
Start with the LLM fact checking guide, then follow the fieldnotes below. All examples are illustrative and each article points to its own editorial source.
LLM evaluation
Choosing an AI LLM fact-checking service: a practical evaluation
Compare evidence boundaries, evaluation results, privacy questions, review effort, and integration requirements.
LLM evaluation
LLM fact checking: evaluate what an answer actually supports
Build a claim-level evaluation process that separates retrieval gaps, assessment errors, and missing evidence.
Follow the claim. Find the evidence.
Go from a better question to a clearer evidence trail—one fieldnote at a time.