What LLMs Actually Do
Large language models are systems trained to work with language and adjacent modalities by predicting likely continuations from context.
In product terms, they are especially useful for:
<ul>
<li>drafting</li>
<li>summarizing</li>
<li>rewriting</li>
<li>extracting structure from text</li>
<li>answering questions with the right context</li>
<li>powering assistant-like interfaces</li>
</ul>

Which product task is a strong fit for an LLM?

LLMs are deterministic knowledge databases that always return the same factual answer to the same prompt.

What LLMs Are Strong At
LLMs are strong when the task involves language transformation or reasoning over provided context:
<ul>
<li>turning messy notes into structured summaries</li>
<li>rewriting text for a different audience</li>
<li>classifying text into categories</li>
<li>answering grounded questions over provided material</li>
<li>suggesting code or content drafts</li>
</ul>

Which workflow best matches an LLM's strengths?

LLMs can still be highly valuable even when a human reviews the final output.

Where LLMs Need Help
LLMs can be wrong, overconfident, inconsistent, or unaware of private and changing information unless the product gives them the right context.
PMs should account for:
<ul>
<li>hallucinations</li>
<li>grounding gaps</li>
<li>latency and cost</li>
<li>failure handling</li>
<li>permissions and privacy boundaries</li>
</ul>

What is often the best first step to improve trust in an internal-policy assistant built on an LLM?

A slightly weaker but faster or cheaper model can be the better product choice if it still meets the task quality bar.

What PMs Own in LLM Products
A strong PM defines:
<ul>
<li>the task and segment</li>
<li>evaluation criteria</li>
<li>review and fallback behavior</li>
<li>data and permission boundaries</li>
<li>the operating metrics after launch</li>
</ul>
LLM features are probabilistic systems. They need product management, not just model access.

Which launch plan is strongest for a new LLM feature?

PMs should treat LLM features as probabilistic systems that need instrumentation, evaluation, and recovery paths.