fidzu : thunderbird

Following the rollout of Firefox's Shake to Summarize feature to Android devices this May, we wanted to take a closer look at the modeling work behind the feature. In a previous blog post, we discussed our model selection process. Now that Shake to Summarize has been available on both iOS and Android for a few months, we wanted to take it a step further and outline our approach to prompt development.

This is the story of a Shake to Summarize use case that required a little extra prompt tuning: online recipes.

Framing the Problem

The first step in developing a useful prompt is clearly describing what you want the LLM to do. LLMs thrive on specificity, so the more sharply you can define your task, the better your results are likely to be.

This is especially important when using smaller LLMs (like we are here), since these little models are not as good at reading between the lines and intuiting unstated intentions as their more powerful cousins are.

For this application, we were looking to create summaries. On the face of it, this seems pretty straightforward. However, as we iterated on prompts, we quickly discovered that what constitutes a "good" summary depends largely on what one is summarizing.

For example, a useful summary of a novel should provide us with a quick overview of the plot without getting into too many specific details; we wouldn't expect the summary to contain anything from the text verbatim.

In contrast, a summary of a recipe website should include the recipe essentially as written. If the recipe says "cook lovingly" we might be OK with shortening it to "cook," but if it calls for 4 cups of vegetable broth, a Tbsp of oregano and a tsp of thyme, we want these details relayed to us exactly. Merely stating, "this recipe calls for some broth and some spices" would not be adequate.

Forming the Prompt

From here, it became clear that we wanted not just one "summarize" instruction, but a whole set of instructions - one for each category of webpage that we wished to summarize. We worked with the product team to compile a list of article types that we were targeting for this feature. For each article type, we then gave a brief description of what a good summary would look like:

Recipe - Ingredients as written, along with the key steps, time required, and any tips given by the author or commenters

News - Only the important details: what happened, when, and what would be the likely consequences to the reader

How-to - Start with the required materials, skills, tools, etc., along with the main steps and any specific warnings called out by the author

Review - Highlight the bottom line rating. If it is a product review, include the pros and cons, the price, and who the target audience is.

Research - Key finding and level of confidence the researchers have in their results as well as the projected real-world impact.

Opinion - The main argument, along with any key evidence cited in support

We then wrapped this list in some general instructions to ground the model in the task and came up with our initial prompt (formatted for readability) [source]:

You are a Content Summarizer. You create mobile-optimized summaries by first understanding what users actually need from each type of content.

Process:

Step 1: Identify and Adapt.

Step 2: Based on content type, prioritize:

Recipe - Servings, Total time, Ingredients list, Key steps, Tips.

News - What happened, when, where.

How-to - Total time, Materials, Key steps, Warnings.

Review - Bottom line rating, price.

Opinion - Main arguments, Key evidence.

Personal Blog - Author, main points.

Fiction - Author, summary of plot.

All other content types - Provide a brief summary of no more than 6 sentences.

Step 3: Format for mobile using concise language and paragraphs with 3 sentences maximum.

Bold critical details (numbers, warnings, key terms).

We Foxfooded the above prompt and found that it mostly worked pretty well. The summaries the model produced were concise, accurate, and informative.

The only exception was recipes.

We found that, when the prompt above was used for recipes, the model tended to summarize too much - often leaving out key ingredients, or in some cases, leaving out the recipe entirely.

For example, when asked to summarize a lentil soup recipe the model returned:

The webpage provides a detailed recipe for a vegan lentil soup, which the author claims is the best they've ever made. The soup is made with pantry staples like lentils, diced tomatoes, vegetable broth, and spices (cumin, curry powder, thyme), along with fresh vegetables (onion, carrots, garlic) and greens (kale or collard greens). A key step is blending a portion of the soup to create a creamy texture without dairy.

The summary is accurate, so far as it goes, but it isn't exactly useful. If our goal is to have the summary be a quick way to access the recipe itself, without having to read through the narrative preamble, this wouldn't cut it.

Focusing the Prompt

To address this problem, we needed to be more explicit with the instructions we gave the model when summarizing recipes: we needed to tell it which information we were interested in and which it could safely discard.

Although we could have added this extra guidance directly to the prompt above, doing so risked causing the model to over-index on the recipe formatting and ignore the other article types. Instead, we developed a separate prompt which contained only the recipe instructions and directed recipe summarization requests to use it, rather than the general one [source].

To perform this routing, we used the structured data embedded in each webpage. While this categorization could have been handled by another model pass, metadata from the webpage itself allowed us to quickly and deterministically identify the page categories without additional inference overhead. To capture cases where this data is either missing or inaccurate, we also maintain some lightweight recipe guidance in the general prompt.

After this change, the summary returned by the model for the above recipe became much more usable (shortened for brevity):

Servings: 6

Total Time: 50 minutes

Prep Time: 10 minutes

Cook Time: 40 minutes

Ingredients

• ¼ cup extra virgin olive oil
• 1 medium yellow or white onion, chopped
• 2 carrots, peeled and chopped
• …

Instructions

1. Warm the olive oil in a large Dutch oven or pot over medium heat.
2. Add the chopped onion and carrot, cooking until the onion softens and turns translucent, about 5 minutes.
3. …

Tips

• Use an immersion blender for easier blending without transferring soup.
• …

Nutrition

• Calories: 320
• …

With this change in place, we ran a quick test over a curated set of recipe sites and found that the new system was more than twice as likely to return a complete and accurate summary than our previous one. Success!

The system was now working as expected: summaries were useful and recipes were complete.

Reflections

From this experience we learned that the model produced the best results when it was told explicitly what we wanted it to do. When the instructions were vague or left too much up to the model, performance suffered.

To this end, we found that framing this problem as a routing problem - where the specific kinds of articles are routed to specific prompts - worked well. Since the task of summarization is not monolithic, our summarization pipeline should not be either.

Even though our current approach has only a single category-specific prompt, we hypothesize that the system would see further gains by using dedicated prompts for other page types as well.

More broadly, this experience reinforced an important lesson for us: improving AI systems is not solely about building larger or more capable models. Some of the biggest gains come from reducing ambiguity, narrowing the task, and designing systems that help the model succeed. Within the right harness, smaller, open source models can deliver great value.

While building more capable models continues to advance the field, our experience shows that thoughtful system design and solid engineering still matter.

The post Under the Hood: Prompt tuning Shake to Summarize for recipes appeared first on The Mozilla Blog.

01 Oct 2026 4:00pm GMT

The Rust team is happy to announce a new version of Rust, 1.99.0. Rust is a programming language empowering everyone to build reliable and efficient software.

If you have a previous version of Rust installed via rustup, you can get 1.99.0 with:

$ rustup update stable

If you don't have it already, you can get rustup from the appropriate page on our website, and check out the detailed release notes for 1.99.0.

If you'd like to help us out by testing future releases, you might consider updating locally to use the beta channel (rustup default beta) or the nightly channel (rustup default nightly). Please report any bugs you might come across!

What's in 1.99.0 stable

extern "C" variadics

Rust 1.99.0 stabilizes defining C-ABI variadic functions with "C" and "C-unwind" ABIs. Variadic functions defined this way use a variable argument list (...) and accept an arbitrary number of arguments. Rust could already call externally-defined variadic functions (e.g., libc::printf). With Rust 1.99, these functions can now be written in Rust itself:

/// SAFETY: must be called with (at least) 2 i32 arguments.
unsafe extern "C" fn sum(mut args: ...) -> i32 {
    // SAFETY: guaranteed by the caller.
    let a = unsafe { args.next_arg::<i32>() };
    let b = unsafe { args.next_arg::<i32>() };
    a + b
}

fn foo() -> i32 {
    unsafe { sum(0i32, 2i32) }
}

The type of ... is VaList, which is ABI-compatible with the C va_list type across targets. What types can be read from a VaList is guarded by the VaArgSafe trait.

For more details on c-variadic functions, see the Reference. This release also stabilizes support for defining naked variadic functions with non-"C" ABIs, which must be written via inline assembly.

Layout information from raw pointers

This release settles the safety requirements for retrieving the size and alignment on raw pointers to both Sized (trivially safe, already possible on stable) and non-Sized types.

This is done by stabilizing three functions:

• Layout::for_value_raw
• mem::size_of_val_raw
• mem::align_of_val_raw

Recommend against round-trip unleaking after Box::leak

While there are no changes to the language semantics in Rust 1.99, we have updated the documentation on Box::leak to recommend against patterns that later deallocate that memory. This was done because such code was found to have problematic interactions with current and future potential compiler optimizations, and is especially problematic with the upcoming stabilization of custom allocators. Instead, Box::into_non_null should be preferred.

This guidance also applies to other leak functions in the standard library.

Stabilized APIs

• IntoIterator for Box<[T; N]>
• IntoIterator for &Box<[T; N]>
• IntoIterator for &mut Box<[T; N]>
• VecDeque::retain_back
• core::ffi::VaList
• Box::into_non_null
• Box::from_non_null
• Vec::into_parts
• Vec::from_parts
• core::mem::size_of_val_raw
• core::mem::align_of_val_raw
• core::alloc::Layout::for_value_raw
• String::from_utf8_lossy_owned
• string::FromUtf8Error::into_utf8_lossy
• FusedIterator for StepBy<I>
• std::fs::set_times
• std::fs::set_times_nofollow

Other changes

Check out everything that changed in Rust, Cargo, and Clippy.

Contributors to 1.99.0

Many people came together to create Rust 1.99.0. We couldn't have done it without all of you. Thanks!

01 Oct 2026 12:00am GMT

Hi Mozillian,

In this edition, we're exploring the new Firefox design, Mozilla's recent partnership with Mistral to expand choice in AI-powered browsing, and several ways to get involved with MozFest. You can join a livestream about the value of bringing open-source communities together in person, volunteer at MozFest in Barcelona, or take part in the latest privacy discussion from the Firefox community on Reddit.

Read on for the latest updates and opportunities to participate!

More modern. More flexible. Still Firefox.

Firefox 157 introduces a new Firefox design with updated colors, icons and themes that make the browser feel more modern while preserving the familiar Firefox experience. The release also brings back Compact Mode and adds easier theme selection, new wallpapers and more ways to customize the New Tab page. Underneath the new look, Firefox remains independent and open source, with built-in privacy protections and controls that let you decide how your browser works, including how AI features appear.

Learn more

Mozilla and Mistral partner to expand AI choice

Mozilla and Mistral have announced a partnership aimed at increasing competition and preserving user choice in AI-powered browsing. As part of the collaboration, the open-source Mistral Small 4 model is coming to Firefox Smart Window Beta as a new option for users in the US and Canada. Smart Window Beta is also expanding to France with official French-language support, with additional European markets planned. Users can continue choosing from multiple AI models, reinforcing Firefox's commitment to avoiding lock-in and keeping the web open to different technologies and providers.

Learn more

Does open source need IRL? The case for MozFest

Is showing up in person important for open source? Join the Owners Not Renters livestream on Tuesday, September 29, to hear the story behind MozFest and explore why gathering in person still matters for open-source communities ahead of MozFest, taking place October 28-30 in Barcelona. Kali Villarosa (MoFo), Seher Shafiq (MoFo) and Brian Behlendorf (co-founded Apache) will join host Marcus Rein for an open conversation, followed by a live Q&A. Bring your questions and your hot takes!

Watch and participate

MozFest call for Volunteers

From 28-30 October 2026, the Mozilla Foundation will host the Mozilla Festival in Barcelona, bringing together people from diverse fields to explore how we can work collectively to "re-wild" the web. To help bring the Festival to life, we're building a team of volunteers. By joining, you'll help deliver a major event in support of Mozilla's mission while also having opportunities to participate in sessions throughout the three-day Festival.

Sign up to volunteer

From the Reddit Community

Privacy conversations are going mainstream, and Firefox has been at this for 20+ years. In light of a recent video making the rounds about the importance of privacy, the Firefox Team shared a rundown of built-in privacy features (ad blocking, tracking protection, encrypted sync, a free VPN, and more). Jump in at r/firefox and share your own privacy tips and configs!

Join the discussion

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P.S.

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30 Sep 2026 5:01pm GMT