In 2025, training AI and Large Language Models is no longer just about scale – it’s about the quality of the signal. Artificial intelligence (AI) is a field within computer science focused on creating systems that mimic human cognitive functions. While early AI development was focused largely on sheer volume, introducing as much content and context as possible, the current generation of machine learning models demands quality. If you’re iterating a service based on or interacting with an LLM and its existing model architecture, the differentiation isn’t just access to computational resources – it’s access to golden data.
While generic datasets scraped from the open web, much of which consisted of unstructured data, sufficed for pre-training and early model development, they fall short of the lofty goal of modern LLMs – human-like text, domain-specific applications, and quality inference. Early training relied on a vast amount and massive amounts of data to build these models. Foundation models now serve as the underlying architecture for LLMs and other AI applications, enabling broad customization across use cases. What is really needed is contextual and useful data that can help the LLM be more accurate and effective. The process of building AI systems involves training models to perform specific tasks, and these models are designed to accomplish each task effectively. LLMs can now generate not only text but also other forms of content, demonstrating their versatility. Google Search is a practical example of narrow AI, using artificial intelligence to perform specific tasks like information retrieval. While AI can analyze and process data at scale, there are still key differences between the capabilities of AI and humans, especially in reasoning and decision-making. That’s where Speedscale steps in.
Today, we’re going to talk about how Speedscale’s ability to capture real-world interactions can lead to structured and useful training data. We’ll look at how this data can be used to fine-tune models to focus on what really matters – your users, your domain, and your inputs.
Introduction to Artificial Intelligence
Artificial intelligence (AI) is transforming the way we approach complex problems by enabling computer systems to perform tasks that once required human intelligence. From learning and reasoning to perception and decision making, AI systems are designed to process and understand vast amounts of data, uncovering patterns and generating valuable insights. At the heart of this revolution are machine learning models—especially large language models—which excel at understanding natural language, making predictions, and generating text that closely mimics human communication.
The rapid growth of AI has been fueled by advances in deep learning, the availability of massive datasets, and ever-increasing computing power. As a result, AI is now embedded in a wide range of industries, automating tasks, enhancing decision making, and unlocking new possibilities for knowledge discovery. Whether it’s analyzing medical records, optimizing financial portfolios, or powering virtual assistants, AI systems are reshaping how we interact with data and solve real-world challenges.
The Case for Gold Data
First, let’s state the obvious – not all data is good data. Data owners tend to think about data sources as a big corpus of material that is generally of the same value. The reality is that training LLMs on large, noisy data sources introduces a lot of useless content that does not improve the LLM in any meaningful way. Worse, it can introduce significant negative patterns into the mix, with harmful content, outdated information, and missing structures that are necessary for instruction tuning or reinforcement learning with human feedback (RLHF). Additionally, poor data makes finding errors in model outputs and training workflows much more difficult, reducing the reliability and accuracy of the resulting models.
Ultimately, training LLMs on low-quality or unrelated data leads to significant issues, including:
- Higher computational costs and GPU utilization per training epoch
- Worse performance on evaluation benchmarks
- Poor results for Low Rank Adaptation training, blocking specific outcomes for model training
- Poor generalization to unseen data, resulting in degraded performance in the model’s ability to infer from new data
- Increased likelihood of toxic or unhelpful outputs, especially in more generalised text generation tasks
This poor data can have long-term tack-on effects resulting in poorer performance, especially in more complex large language model training processes requiring data to be processed through several layers and multiple nodes in deep neural networks, and a complex transformer model or specificity compliance. The transformer model itself is a type of artificial neural network composed of multiple nodes and several layers, enabling advanced pattern recognition and data processing. LLMs and natural language processing already introduce significant engineering challenges, so choosing to use poor data only multiplies these challenges tenfold. Neural networks, especially deep neural networks, are fundamental to the architecture of LLMs, providing the backbone for their ability to learn from vast amounts of data by passing information from one layer to the next layer.
By contrast, capturing production traffic from your APIs, endpoints, or chat interfaces gives you access to high-value text samples that reflect how users actually interact with your model. These are ideal for supervised fine-tuning and evaluating model performance across multiple dimensions, giving significant (and critical) boosts to relevance, helpfulness, tone, accuracy, and more.
The Role of Artificial Neural Networks
Artificial neural networks (ANNs) are the backbone of many modern AI systems, including large language models that drive today’s natural language processing breakthroughs. Inspired by the human brain, ANNs consist of multiple layers of interconnected nodes—often called neurons—that work together to process and transmit information. By training these networks on large datasets, AI models learn to recognize patterns, understand context, and perform complex tasks with remarkable accuracy.
One of the most significant advancements in this field is the transformer architecture, which has revolutionized how language models process and generate natural language. Transformers leverage multiple layers and attention mechanisms to capture relationships within data, enabling highly accurate text generation and understanding. Beyond language, artificial neural networks are also foundational in computer vision, speech recognition, and other domains, making them essential for building powerful, adaptable AI systems that can tackle a wide variety of tasks.
Speedscale: Turning Real Interactions Into Training Assets
With the value of data having such obvious impacts on your model’s efficacy, the question then becomes how one improves this data quality. Thankfully, Speedscale stands as a great solution to this problem. At its core, Speedscale is built to observe, replay, and transform API interactions – and while it’s traditionally built for traffic mocking and load testing, it is this functionality that can give us a powerful way to boost LLM training processes. As one of the essential AI tools, Speedscale enables trustworthy and transparent AI governance by helping manage, monitor, and ensure the accountability of AI models and activities.
When improving LLM training processes, prompt engineering plays a crucial role in optimizing model outputs, enhancing accuracy, and reducing biases. Additionally, building better models relies on training models with high-quality data to ensure robust and reliable performance.
Let’s dive into how Speedscale can make this process better!
1 – Capture Raw Data from Live Systems
Speedscale continuously captures real-world input text, user queries, and corresponding responses from your production systems. This sequential data forms the backbone of domain-specific training datasets. During model training, this data is broken down into input tokens, which are the basic units the model uses to learn language patterns. LLMs are trained to predict the next token and next word in a sequence, enabling them to generate coherent responses. This process allows LLMs to answer questions effectively by understanding and responding to user queries. In essence, you’re working backwards from the end state – if you want your model to understand your data, you need to provide it with real data from your system.
The best part of this process is that there’s no need to generalise this data or generate any traffic patterns – if you want your models to do something specific based on specific variables in your observed traffic, you can simply provide it with an example of that observation as direct training!
ALT: With Speedscale, you can connect diverse and varied systems into a cohesive live data set
2 – Filter for High-Signal Exchanges
Using Speedscale also unlocks the ability for you to identify traffic that is potentially highly valuable training data. Using different rules, you can filter captured traffic for:
- Specific domains (e.g., finance, healthcare, DevOps)
- Long-form completions
- Clear instructions or response patterns
- High feedback scores (if you’re collecting ratings)
This kind of filtering can then let you target specific chunks of data for inference and contextual data mining. For example, this allows you to use your LLM to correlate specific feature utilisation against a specific domain to identify potential areas of improvement. By leveraging filtered data, LLMs can be fine-tuned to perform multiple tasks, such as answering questions, summarizing, and translating languages, demonstrating their versatility and ability to handle a wide range of applications. This reduces the noise of your training data significantly and can help isolate golden instruction tuning examples that align against your domain, your goals, and your development lifecycle.
ALT: Speedscale allows you to capture and filter traffic from a variety of data sources
3 – Structure for Supervised Fine-Tuning
From here, you can export your raw data and structure it for more specialised training pipelines and external models. You can export the data and use it for:
- JSON for prompt/response pairs
- Datasets for instruction tuning phase workflows
- Multi-turn dialogs for conversational agents
- Heuristics analysis
- Regression testing
Structured data from Speedscale can accelerate research and development in AI by providing high-quality datasets for experimentation. Retrieval-augmented generation can also be leveraged in these pipelines to enhance text generation tasks by integrating external data sources, resulting in more accurate and context-aware outputs.
With this system in place, you’ve created a powerful feedback loop, taking production data and converting it into a structured dataset that can then be used to generate a new model checkpoint. You’ve created a golden data lifecycle!
ALT: Speedscale’s traffic replay can allow you to replay your data, testing complex systems like iterative reward model mechanics or transformers library installations
Code Generation and Automation
Large language models are opening up new frontiers in code generation and automation, making software development faster, more efficient, and less error-prone. By being fine tuned on vast repositories of programming languages, these models can generate code snippets, complete functions, and even build entire applications based on natural language prompts. This automation not only streamlines repetitive tasks but also helps developers find errors, optimize code, and accelerate the development process.
AI-powered code generation tools are transforming how businesses approach software engineering, enabling the creation of customized solutions tailored to specific needs. Whether it’s automating routine programming tasks or assisting with code review and debugging, large language models are empowering teams to focus on higher-level strategy and innovation, while AI handles the heavy lifting of repetitive coding work.
The Benefits of Text Generation
Text generation is one of the most impactful applications of large language models, enabling the creation of high-quality content at scale. From articles and reports to social media posts and personalized messages, AI-driven text generation streamlines content creation, ensuring consistency and saving valuable time. These models can generate text in multiple languages, making it easier for businesses to reach global audiences and communicate across cultural boundaries.
By leveraging large language models for text generation, organizations can enhance their content marketing strategies, improve customer engagement, and unlock new creative possibilities. The ability to produce tailored, relevant content on demand not only boosts efficiency but also drives better business outcomes by delivering the right message to the right audience at the right time.
Real-World Use Case: Domain-Specific Instruction Tuning
Let’s look at a practical use case where this sort of approach makes sense.
Imagine you’re building a customer service assistant for an enterprise SaaS product. The LLM needs to handle technical troubleshooting, pricing questions, and product onboarding. Your LLM is currently using pre-trained models, and when you try to use it to answer your customers, the instructions seem overly vague or reference documentation that is only partially applicable to the issues at hand.
When you sit down and apply an evaluation framework, you discover two core issues:
- Issue 1 – Your specific implementation of the open source solutions in your codebase is meaningfully different from the standard implementation, with custom transformers, gateways, etc., resulting in complex pathways. In these custom transformers, the attention mechanism plays a crucial role by enabling the model to focus on relevant parts of the input data, which improves context understanding and prediction accuracy. Unlike recurrent neural networks, which process sequential data by maintaining memory of previous inputs, transformer models leverage the attention mechanism to handle context more efficiently and in parallel.
- Issue 2 – You notice that your consumers are asking some quite complex questions – e.g., how much a certain traffic spike might cost, which your model has no real specific data on
By using Speedscale, you can resolve both of these issues. Firstly, capture your traffic. This traffic can be ingested both inbound and outbound, allowing you to see the full snapshot of your traffic in practice.
From here, you can start to resolve your first issue by identifying which services users are using for specific functions. By observing traffic load, you can identify potential pathways that can be simplified using some LLM-powered behavioural analysis, as well as those pathways that are seeing misuse – this suggests a failure of documentation or a need to reroute resourcing and functions.
Next, you can use this traffic and inferred behaviour to then train your AI LLM for more accurate functions. If you know how much a specific server action costs because of another client, you can use that same data to provide the best estimate for another client, for instance.
This results in a model that allows you to use actual data interactions with the service to provide an LLM that is accurate to your observations rather than to generic answers.
Overcoming Repetitive Tasks
Repetitive tasks like data entry, document processing, and routine customer service can drain valuable resources and limit productivity. Large language models offer a powerful solution by automating these tasks, freeing up human talent for more strategic and creative work. Trained on diverse datasets, these models excel at text classification, sentiment analysis, and entity recognition, enabling businesses to automate workflows and reduce manual effort.
By integrating large language models into their operations, organizations can improve accuracy, minimize errors, and deliver faster, more reliable service. Automation powered by AI not only streamlines processes but also allows for the development of customized solutions that address specific industry needs. As a result, businesses can focus on innovation and growth, confident that routine tasks are handled efficiently and effectively by advanced AI systems.
Closing Thoughts – Better Data, Better Models
Large models like ChatGPT or Claude need high-quality data, and while the cost of data preparation might seem onerous, the reality is that quality data is highly attainable at a cost that might be far less than you think. These data units can help deliver some key benefits, including:
- Helping your Large Language Models LLMs be more accurate, eliminating guesswork in the data pipeline
- Make for more accurate and helpful responses based on what the model learned from your own data
- Improve real-world applications in raw resource utilization terms, reducing the overall cost to implement LLMs at scale
The fundamental truth is that you don’t need to train on the entire internet to get better performance. You need data that reflects the use cases you care about. Training LLMs on low-signal, generic datasets will only get you so far, but accurate data can unlock incredible operational success/
Speedscale enables a high-signal training workflow that can unlock your LLMs’ true potential at scale and at cost. Whether you’re focused on text classification, human feedback loops, or transfer learning for domain-specific LLMs, Speedscale helps you train on gold, not garbage – and you can get started with a free trial today!
