{mode === "takeaways" && }
{mode === "eli5" && }
{mode === "executive" && }
);
}
```
The three modes:
- **Key Takeaways** — bullet-point extraction (default)
- **Explain Like I'm 5** — simplified explanation, generated separately with a different prompt
- **Executive Summary** — two-paragraph overview
Each mode uses a different prompt, but all follow the "extract, don't generate" rule.
---
## The Verified Context Badge
The green dot at the bottom of the summary card isn't decorative. It indicates that the summary passed the verification check:
```python
def verify_summary(summary: str, original: str) -> bool:
claims = extract_claims(summary)
for claim in claims:
if not any(claim.lower() in original.lower() for original in original.split(". ")):
return False
return True
```
**Explanation**
- The function `verify_summary` takes two string arguments: `summary` and `original`.
- It uses a helper function `extract_claims` to extract claims from the `summary`.
- For each claim, it checks if the claim (in lowercase) is found in any of the sentences of the `original` text (also converted to lowercase).
- If any claim is not found, the function returns `False`; otherwise, it returns `True` after checking all claims.
- This is useful for verifying the accuracy of summaries against original content.
Each claim (extracted sentence) must be found in the original text. If any claim isn't found, the badge shows orange instead of green, and a warning is appended to the summary: "Some claims could not be verified against the source."
---
## Error Handling
If the LLM call fails (API error, timeout, rate limit), the component silently degrades:
```python
async def get_summary_safe(post_id: str) -> str | None:
try:
return await generate_summary(post_id)
except Exception as e:
logger.warning(f"Summary generation failed for {post_id}: {e}")
return None
```
**Explanation**
- Defines an asynchronous function `get_summary_safe` that takes a post ID as a string argument.
- Attempts to call the `generate_summary` function to retrieve a summary for the specified post ID.
- If an exception occurs during the summary generation, it logs a warning message including the post ID and the error details.
- Returns `None` if the summary generation fails, ensuring the function handles errors gracefully.
- Utilizes Python's type hinting to indicate that the return type can be either a string or `None`.
The frontend checks for null and renders nothing — no error message, no broken card, no user-facing failure. The post works perfectly without the summary.
---
## Cost
At ~4000 tokens per summary and $0.15/1M tokens, each summary costs about $0.0006. With ~20 posts and regenerating only when content changes, the monthly cost is negligible — less than a cent.
The caching layer (24-hour TTL) ensures the same summary isn't regenerated on every page load. In practice, each summary is generated once and served from cache for 24 hours.
---
## What's Next
The next post covers the GEO (Generative Engine Optimization) stack — how llms.txt, ai-profile.json, structured data, speakable markup, and articleBody work together to make your content accessible to AI crawlers.
---
*Built with FastAPI, multi-provider AI (GPT-4o-mini, Gemini 1.5 Flash, Llama 3.2), Redis caching, and zero third-party CMS.*