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**AI models are everywhere, but sometimes, even the best LLMs produce bafflingly random outputs. Why? And more importantly—how can you tweak it?**

In 2024, the AI landscape has been evolving at breakneck speed. The race to release new models is relentless, with cutting-edge architectures dropping almost weekly. [aiworld.eu](https://aiworld.eu) and [Hugging face](https://huggingface.co) teamed up [to analyze the trends](https://huggingface.co/spaces/huggingface/open-source-ai-year-in-review-2024?day=3), tracking cumulative likes on models over 48 weeks. The data tells a clear story: the market is moving faster than ever, with new features, optimizations, and surprises constantly reshaping the AI ecosystem. 

![](https://www-uploads.scaleway.com/Capture_d_ecran_2025_03_17_a_14_14_50_82569b4dcf.png)

But with all this innovation comes confusion. AI models and the APIs that run them are still quite new for a lot of people and their usage can sometimes be puzzling.  

## Why do AI models sometimes behave unpredictably? How can you regain control? And finally, how can you ensure model accuracy at scale?
Let’s break it down.

**Why do AI models sometimes behave unpredictably?**

One of the biggest surprises? **LLMs aren’t built to be fully deterministic**. That means, even for simple questions like “1+1=?”, you might get unexpected variations. This isn’t a bug—it’s a feature. Some applications thrive on creative responses, while others demand strict, repeatable outputs. LLMs have not been built to be deterministic as any other algorithms, there are many use cases for which we want the LLM to generate creative answers and many others for which we look for a clear deterministic answer 

**Regain control: how to get deterministic outputs for a model?**

Adjust the determinism of the model: 
- **Temperature:** put the temperature at 0, which is the equivalent to tell the LLM to stop being creative. 
- **Seed:** provide a fixed seed, force the 1st tokens to be something you determine. If you provide this seed and temperature is 0, then the tokens that follow will always be the same

While LLMs can generate random lists of words without meaning or grammatical structure, this output is generally not useful. The ideal “creativity level” is something in between completely deterministic and completely random. This can be adjusted through:
- **Temperature:** put the temperature at 0.7. Overall values between 0.5 and 1 will add different levels of randomness. Values above 1 should usually be avoided, as they will usually output completely random words after a few paragraphs (even mixing languages).
- **Top P:** Set up a high top p (eg. above 0.7). This value influences the diversity of output, which is equivalent for the model to have a wider vocabulary. As an example, if you ask the model “List 10 known but original colors”, the model will provide:
Colors like “Mocha” or “Lavender” with a top_p of 0.01, because these words are widely used overall in the language.
Previous colors, and additional ones like “Amber”, “Taupe”, “Caramel”, “Cobalt” with a top_p of 1, which are less frequent in english language 
- **Prompts:** Besides changing typical LLM settings, you can also engineer your prompts directly, to guide the LLM to provide original outputs. Sometimes adding prompts such as “Provide creative outputs” is enough to change a lot of results.

Want to take control of your LLM’s randomness? Play around with different models and settings on [Generative APIs](https://www.scaleway.com/fr/generative-apis?utm_source=website&utm_medium=organic&utm_campaign=202503-cli-ww-all-gen_api_ga&utm_content=blog_post_genapi_). Your first million tokens are free. Experiment, tweak, and see the difference for yourself.

<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/Iv3qpGkdFQk?si=nvoeUC1lFGXWSwJE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

Be careful—modifications can have unpredictable impacts at scale. Whether you're tweaking your model for more deterministic output, adjusting prompts for accuracy in a specific feature, or relying on a model builder who may be deprecating or modifying the model without your knowledge, it's crucial to validate changes. You need to check the accuracy of modifications at your application level, as they can introduce hidden bugs in your workflow that may only surface in certain use cases.

## Model accuracy at scale: how to ensure a different LLM version or configuration will not break my application?

Setting up an AI application (whether it’s a Chatbot or a RAG- based (Retrieval-augmented generation) Agent) usually requires a lot of effort at all steps of the life cycle: prompt engineering, RAG pipelines retrieval accuracy, function calling trigger sensitivity, manual testing…
Performing all these steps again with a newer LLM version can be tedious but is necessary to avoid regressions in your application.

Indeed, there are usually no guarantees that a newer LLM version (even a minor one) or configuration will provide the same output quality for all prompts on all thematics. 

However, you can still limit the risk by:
- **Using models which are fine-tuned versions of the previous model** you were using. Similar output is not fully guaranteed, but will usually differ less than a completely different model.
- **Setup automated testing pipelines on sample dataset:**
- **Based on LLM classification** (also known as “LLM as a judge”): you can ask an LLM (the same one, or a bigger and thus more accurate one) to classify the quality of the output. This solution is not perfect, but can help detect many issues preventively.
- **Based on strict / rule-based verification:** if the output of the LLM can be verified with simple algorithms, you should definitely implement these checks. This can be done either with:
- **Raw outputs (eg. text)**, such as code. If your LLM generates code, you can try running this code, and if it doesn’t work, you know for sure the output is incorrect.
- **Structured outputs (eg. json output)**, such as structured representation of the content or intent. For instance, if you try to digitize and process a picture of a bill, you should use structured outputs, and have the LLM output each line of the bill and the corresponding total. You can then check if the total matches the sum of all the lines. Again if you total doesn’t match, you know for sure you cannot trust the output.

_Note that in both cases, the checks performed will only ensure the content may be correct (as in “looks plausible”). There is no 100% guarantee the content is correct (eg. in the code example, the code may run, but without doing what you expected). Since by design, LLMs provide “plausible” outputs you should be particularly careful of this. Models can indeed easily create hallucinations which appear consistent (eg. in the billing example, total bill lines may match, but one line may have been modified to compensate for an error in another one)._

- **Perform manual testing at scale:** Many applications developers still choose to perform A/B tests on users samples. Although this can take some time and be costly, it is the most reliable way to avoid regressions on a wide variety of usage.

Although advances in generative AI continue to push the boundaries of what’s possible, they also raise significant concerns about the reliability of models and the accuracy of their outputs. LLM hallucinations, far from being simple technical glitches, serve as a reminder that these systems are not oracles of truth. Additionally, they highlight how these models can reflect and perpetuate biases present in the training data.
As AI becomes increasingly integrated into decision-making processes, projects must take into account the ethical implications in the response error rate of these technologies. To address these concerns, research-driven initiatives such as prompt analysis and model testing are emerging, aimed at making AI more explainable. Learn in our next article how we can identify and mitigate biases in AI outputs, ultimately working toward more reliable and fair responses.

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Why is my state-of-the-art LLM spitting out nonsense?

## How can French and European companies stand out and remain competitive?

Killian Vermersch, CEO of Golem.ai, shares his vision of AI in Europe, emphasizing the importance of local innovation, frugality, and explainability. Focusing on the insurance sector, he discusses the challenges of European technological sovereignty, regulatory issues, and the opportunities presented by ethical and accessible AI, while highlighting the growing impact of the digital workplace on the jobs of tomorrow.

## What are the main trends you observe in France regarding the digital workplace this year?

In the workplace, AI does not replace employees but assists them. "Today, employees and companies share the same goal: AI that reduces repetitive tasks and improves the quality of interactions," notes Killian Vermersch.

This shift is particularly evident in managing incoming workflows. [Golem.ai](https://golem.ai/en/) automates the sorting and analysis of customer requests, allowing employees to be more efficient and accurate. "One of our B2B e-commerce clients reduced their response time to commercial inquiries by a factor of six," he illustrates.

Two principles guide Golem.ai's work: frugality (solutions that are energy-efficient and much lighter than generative AI) and explainability (through transparent and traceable systems). This approach is particularly valued in sectors such as insurance and the public service, where understanding AI-driven decisions is crucial.

Killian Vermersch illustrates the need for regulation by drawing a parallel with aviation accidents: "We often hear about the safety of Airbus versus Boeing planes. In reality, strong regulations and enhanced transparency standards have allowed Airbus to better control its systems and improve safety. AI in Europe is following a similar trajectory."

For example, the AI Act, which regulates AI usage in Europe, requires insurers to justify automated or AI-assisted decisions. Unlike in the U.S., where AI can approve or deny a claim without clear justification, European insurers must guarantee transparency in every process. This is a useful constraint that strengthens trust and **can even boost the economy in the long run.**

## In the face of global tech giants, how can French and European companies leverage AI to stay competitive?

"France tends to be self-critical about its technological lag. But why do we have this lag? Too often, we assume that American solutions are superior and should be adopted by default. If we reversed this mindset, we could change the dynamic and strengthen our competitiveness," explains Killian Vermersch.

According to him, the key lies in a virtuous cycle: the more French and European companies adopt local solutions, the more they foster growth and improvement. "We have a distinct French innovation that combines frugality and ethics." At Golem.ai, this translates into a language analysis and understanding technology that is both sustainable and transparent.

## From Proof of Concept to Industrialization

The era when AI was merely an experimental topic is over. Today, it's no longer about testing AI for its own sake but ensuring a tangible return on investment for clients. The Proofs of Concept (POC) conducted by Golem.ai are designed to address specific business use cases with measurable impact from the outset.

## How does Golem.ai support local innovation by collaborating with other industry players?

Golem.ai is committed to promoting European artificial intelligence, tailored to the continent's regulatory and linguistic realities. "Unlike Anglo-Saxon solutions, our AI natively supports more than 30 languages without intermediate translations. This is a significant advantage for European businesses," he points out.

Digital sovereignty is also a major issue. "Many French startups build their products on American infrastructures, which weakens our independence. Working with European players like Scaleway helps keep value and expertise within Europe."

In sectors such as insurance, handling data flows is a challenge. Take automobile claims, for example: analyzing a variety of complex documents, such as accident reports, in record time is crucial. These documents require quick decision-making.

Golem.ai's AI automatically extracts key information from attachments, making decisions more objective while speeding up processing. "This automation improves service quality and speed. An expert can generate a draft response, modify it if necessary, and then send it."

The impact is significant: "We have enabled 1,200 experts to reclaim more than 15% of their total working time. Our goal is to achieve an additional 10% efficiency gain. The return on investment is impressive—20 to 30 times the initial investment—thanks to time savings, reduced errors, and improved customer relations."

## Managing the Complexity of AI Adaptation in Regulated Sectors

Golem.ai serves major clients in the public sector, retail, and insurance industries, where regulatory compliance is particularly demanding.

In the public sector, Golem.ai has launched a document flow management project with the Ministry of the Armed Forces. "When handling public data, security becomes an absolute priority. We have also worked with a major medical order, where we must ensure a system that is both reliable in terms of result quality and data security. In fact, we collaborated with Scaleway to understand the security processes in place."

In the energy and insurance sectors, the need for reliable AI is just as critical. "Insurers are particularly keen on solutions like ours, and in some domains, explainability is crucial. Today, these requirements are becoming standard. We have therefore worked with Scaleway to make our solution operable on their infrastructures."

For AI to be effectively adopted and used by businesses, it must be explainable and aligned with clearly identified business needs. "What can slow down an AI project isn’t just technical constraints, but the lack of clear use cases upfront," emphasizes Killian Vermersch.

Explainability is a key driver: it allows companies to understand AI-driven decisions and build trust. Moreover, it ensures better adoption by employees, offering them visibility into decision-making criteria. Finally, market maturity plays a decisive role. AI must meet customer expectations, anticipate future needs, and, above all, fit into a precise business framework. By combining business expertise with technological performance, we can offer truly impactful solutions.

## Their R&D Roadmap

Golem.ai continues to innovate by integrating different AI approaches, particularly neuro-symbolic AI. "The goal is to make different AI systems interact and gradually enhance our solutions. One of our latest advancements is a vision engine capable of recognizing images and integrating generative AI to expand customer message typologies, broadening the range of applications."

The company is also working on reducing its client onboarding time, which currently takes two to three weeks.

## Golem.ai’s Long-Term Goal in the AI Ecosystem

Golem.ai aims to establish itself in Europe, a multicultural and multilingual market. "Our AI is naturally strong in multilingual capabilities, unlike other solutions that are limited to three or four languages. We have already worked on projects in Poland and Hungary, as well as with many French clients operating internationally." He also notes that GDPR regulations provide a real advantage in this context.

Finally, sovereignty is a central issue. "Many French AIs are built with American technologies and hosted on AWS. A large portion of the investment ultimately goes to Amazon, Google, or Microsoft. We must ensure that European funding is used to develop local solutions and strengthen the European ecosystem."

Killian Vermersch concludes: "Today, everyone is talking about AI, but it is still not widely deployed in everyday operations. The transition from innovation to return on investment depends on performance, frugality, and explainability. 

At Golem.ai, we are committed to these three pillars, in collaboration with partners like Scaleway, who share our values of transparency and energy efficiency." A vision that could very well shape the future of artificial intelligence in Europe.



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