I've been running Claude 3 Haiku through its paces for the past two weeks, specifically for generating internal communications drafts. The speed is undeniable—it's the fastest model I've ever used for batch processing tasks. However, I'm now compiling the data on its output quality, and the accuracy question is becoming central to my evaluation.
My test involved generating 50 distinct internal comms drafts based on a structured prompt template and a set of input variables (project name, key metrics, stakeholders, deadlines). I then had three team members score each output on a rubric covering:
* Factual consistency with provided inputs
* Appropriate tone for internal audience
* Clarity of action items
* Absence of hallucinated details
The quantitative results are revealing:
* **Speed:** Average generation time of 1.2 seconds per draft (vs. 3.5s for Sonnet in my prior test).
* **Factual Consistency Score:** 78% (drafts perfectly matched all provided input variables).
* **Hallucination Rate:** 22% of drafts introduced at least one minor detail not in the source material (e.g., inventing a stakeholder name, slightly misstating a metric).
The hallucinations were subtle but problematic. For example, given an input of "Q2 Revenue Growth: 15%", it might draft a sentence saying "As we discussed in the last all-hands, the 15% growth in Q2 exceeded our 12% target," even though no target was mentioned in the source. This "filling in the blanks" behavior is the core concern.
For now, my team's protocol is to treat Haiku as a powerful first-draft engine, but one that requires a verification layer. We cannot use its output unchecked. The trade-off analysis becomes: does the time saved on generation outweigh the time required for fact-checking?
I'm curious how others are navigating this. Specifically:
* Are you using prompt engineering techniques to curb Haiku's tendency to infer?
* Have you found a class of internal comms (e.g., simple meeting notes vs. complex project updates) where its accuracy is acceptable?
* Is anyone using a hybrid approach, like using Haiku for structure and Sonnet for fact-polishing?