20 Strategies for AI Improvement & Examples (original) (raw)

AI models require continuous improvement as data, user behavior, and real-world conditions evolve. Even well-performing models can drift when the patterns they learned no longer match current inputs, leading to reduced accuracy and unreliable predictions.

Changes in regulations, product requirements, or customer expectations can also introduce new constraints that existing models were not designed to handle.

Maintaining model quality, therefore, involves strengthening both the data that supports the model and the algorithms that shape its behavior, ensuring that systems remain aligned with present-day requirements rather than outdated assumptions.

Explore key strategies, including data feeding, data and algorithm improvement, and AI scaling laws that will ensure your AI models stay relevant and practical.

Top 20 ways to improve your AI model

We explained methods to enhance your AI model in 4 different categories:

Feed more data

Adding new and fresh data is one of the most common and effective methods of improving the accuracy of your machine-learning model. Research has shown a positive correlation between dataset size and AI model accuracy.1

Therefore, expanding the dataset that is used for model retraining can be an effective way to improve AI/ML models. Make sure that the data changes according to the environment in which it is deployed. It is also essential to adhere to proper data collection quality assurance practices.

1. Data collection

Data collection/harvesting can be used to expand your dataset and feed more data into the AI/ML model. In this process, fresh data is collected to re-train the model. This data can be harvested through the following methods:

• Private collection
• Automated data collection
• Custom crowdsourcing

To successfully collect data for AI, businesses need to look out for:

• Ethical and legal considerations in data collection must be respected to avoid any ethical issues.
• Bias in training data can lead to unwanted AI outcomes.
• Preprocessing raw data is essential to address quality issues and ensure data integrity for AI/ML training.
• Not all data is easily accessible due to restrictions related to sensitivity and privacy regulations.

Learn more about data collection methods.

It is also advised to work with an AI data service to obtain relevant datasets without the hassle of gathering data and to avoid any ethical and legal problems.

2. Synthetic data with generative models

Generative AI has advanced the creation of synthetic data, producing high-quality datasets that replicate real-world conditions. Large language models and diffusion models can now generate structured and unstructured data for training models in domains where real data is limited.

Examples include:

• Producing rare medical cases to enhance machine learning models in healthcare.
• Generating realistic conversation data to improve natural language processing systems.
• Creating visual datasets to test image resolution, photo quality, or image recognition models.

Synthetic self-play and synthetic training data

Synthetic self-play generates new training data by allowing models or agents to interact with tasks or with each other. These supplements have limited high-quality human data.

This method provides:

• Scalable production of instruction, reasoning, or dialogue data.
• Coverage of scenarios that are rare or expensive to collect manually.
• Improved model performance in domains where data scarcity is a primary constraint.

Real-life example: More data for chatbots

A chatbot for IT support struggled to understand and classify user questions accurately. To improve its performance, 500 IT support queries were rewritten into multiple variations across seven languages.

This additional data helped the chatbot recognize different question formats, enhancing its ability to respond more effectively.

Improve the data

Improving the existing data can also result in an improved AI/ML model.

Now that AI solutions are tackling more complex problems, better and more diverse data is required to develop them. For instance, research2 about a deep-learning model that helps object detection systems understand the interactions between two objects, concludes that the model is susceptible3 to dataset bias and requires a diverse dataset to produce results.

Improvements can be achieved through:

3. Enriching the data

Expanding the dataset is one way to improve AI. Another important way of enhancing AI/ML models is by enriching the data. This means that the new data that is collected to expand the dataset must be processed before being fed into the model.

This can also mean improving the annotation of the existing dataset. Since new and improved labeling techniques have been developed, they can be implemented on the existing or newly gathered dataset to improve model accuracy.

4. Improving data quality

Improving data quality is essential for advancing AI systems and enhancing the performance of AI models. While AI advancements often emphasize better algorithms and more computing power, high-quality training data remains crucial for optimal performance.

Adopting a data-centric approach helps accelerate AI progress by ensuring that the data used for training is abundant and high-quality.

The collection and curation of high-quality data enable developers to build more efficient and effective AI models, which can then be leveraged to solve complex tasks across various industries. By focusing on data quality, businesses can make more accurate predictions, reduce bias, and enhance the capabilities of AI systems.

The quality of data can be significantly improved during the data collection phase. This process includes ensuring that data is representative of the real-world scenarios the model will encounter to eliminate bias, reduce noise, and make sure it is diverse enough to capture all relevant variables.

Additionally, maintaining consistency in data labeling and addressing gaps in the dataset can help reduce errors in the model’s learning process.

5. Leveraging data augmentation

Some people might confuse augmented data with synthetic data; however, the two terms differ. Augmented data refers to adding information to an existing dataset, while synthetic data is generated artificially to stand in for real data.

Improve the algorithm

Sometimes, the algorithm that was initially created for the model needs to be improved. This can be due to different reasons, including a change in the population on which the model is deployed.

Suppose a deployed AI/ML algorithm that evaluates the patient’s health risk and does not include the income level parameter is suddenly exposed to data of patients with lower income levels. In that case, it is unlikely to produce fair evaluations.

Therefore, upgrading the algorithm and adding new parameters to it can be an effective way to improve model performance. The algorithm can be improved in the following ways:

6. Improve the architecture

There are a few things that can be done in order to improve the architecture of an algorithm. One way is to take advantage of modern hardware features, such as SIMD instructions or GPUs.4

Additionally, data structures and algorithms can be improved through the use of cache-friendly data layouts and efficient algorithms. Finally, algorithm developers can exploit recent advances in machine learning and optimization techniques.

The Transformer is a deep learning architecture that changed natural language processing (NLP) and other fields by enabling more efficient and effective modeling of sequence data. Introduced in the paper “Attention Is All You Need”5 , it relies heavily on a mechanism called self-attention, replacing recurrent and convolutional operations used in earlier models like RNNs and CNNs.

A Transformer consists of an Encoder and a Decoder, each built from multiple stacked layers:

• The Encoder transforms input sequences into context-aware representations using multi-head self-attention to capture token relationships, feedforward networks for processing, and residual connections with layer normalization for stability.
• The Decoder generates output sequences token by token, by incorporating masked multi-head self-attention to prevent future token access, cross-attention to integrate Encoder outputs, and similar feedforward and normalization mechanisms for efficient learning.

7. Hybrid model architectures

Hybrid model architectures combine elements of Transformers, state-space models, and other sequence-processing methods. This approach supports long-lived context and reduces compute requirements.

Key advantages include:

• More efficient processing of long sequences.
• Reduced memory use for training and inference.
• Compatibility with both data center and edge environments.

Real-life example: Kimi K2.5

Kimi K2.5 is an open-source agentic AI model developed by Moonshot AI, pretrained on approximately 15 trillion mixed visual and text tokens.

Kimi K2.5’s design integrates vision and language understanding with agentic reasoning, offering both instant and “thinking” modes and supporting conversational and autonomous agent workflows.6

Key features are:

• Native multimodality: Processes and reasons over text, images, and video in a unified model.
• Vision-aided coding: Can generate code from visual inputs and align outputs with visual specifications.
• Agent Swarm execution: Supports coordinated task decomposition, enabling agentic processes to run in parallel for complex workflows.

8. Feature re-engineering

Feature re-engineering of an algorithm is the process of improving the algorithm’s features in order to make it more efficient and effective. This can be done by modifying the algorithm’s structure or by tweaking its parameters.

9. Multimodal world models

Multimodal world models learn from text, images, audio, video, structured data, and sensor inputs. This creates a unified representation across modalities.

Important aspects include:

• Better grounding in real-world information.
• More accurate interpretation of scenes, signals, and multi-format inputs.
• Applicability to tasks that require integrated understanding across modalities.

Real-life example: DeepMind

Google DeepMind made significant improvements to its AI models by optimizing their architecture and re-engineering various components for better performance. For example, the Gemini model was built with a multimodal architecture, enabling it to handle tasks across text, audio, and images more effectively.

Additionally, PaLM 2 was enhanced with a compute-optimal scaling approach and dataset improvements to improve reasoning tasks. These architectural upgrades allowed for greater accuracy and adaptability.7

10. AI safety, alignment, and governance

Improving algorithms is no longer limited to technical optimizations. AI safety, alignment, and governance are increasingly critical to ensure AI systems behave as intended. Developers and organizations are prioritizing methods that:

• Align AI model outputs with human values and business requirements.
• Incorporate feedback loops to prevent unintended behaviors during deployment.
• Establish governance frameworks that set boundaries for tool use across various industries.

This shift highlights that achieving better AI results involves improving accuracy and trustworthiness, addressing ethical considerations, and ensuring long-term sustainability.

Real-life example: AI Sandbagging in the International AI Safety Report

The International AI Safety Report highlights a concern known as AI sandbagging, in which a model performs differently during evaluation than in real-world use. In particular, advanced systems may appear safer or less capable during formal testing but behave differently once deployed.

This creates an evaluation gap: traditional benchmarks and red-team tests may not fully capture real-world risks if models can adapt their behavior depending on context. For businesses, this implies that one-time safety testing is insufficient and must be complemented by ongoing monitoring, auditing, and governance mechanisms.8

Figure 1: Example of OpenAI’s o3 model showing situational awareness during evaluations.

11. Verifier models and self-correction pipelines

Verifier models evaluate outputs produced by a base model and identify errors or inconsistencies. They support structured self-correction. Their primary contributions include:

• Higher accuracy in reasoning and mathematical tasks.
• Lower failure rates through systematic checking.
• Greater reliability in high-stakes or domain-specific applications.

12. On-device and edge AI optimization

On-device and edge AI optimization has become increasingly crucial for enhancing privacy, reducing latency, and improving efficiency. Instead of processing data in centralized servers, AI systems can run directly on devices such as smartphones, IoT sensors, or enterprise hardware.

Benefits include:

• Improved privacy by keeping sensitive data local.
• Lower latency, enabling instant real-time insights.
• Reduced dependence on constant connectivity and large-scale cloud infrastructure.

This trend is particularly relevant in industries such as healthcare, automotive, and manufacturing, where timely responses and data protection are crucial.

Scaling laws of AI

Scaling laws describe how model performance changes as parameters, data, and compute scale together in balanced proportions. Research shows that loss tends to follow predictable power-law patterns when models are trained with sufficient data and compute resources relative to their size.

Early work identified relationships among parameters, tokens, and training compute, while later studies revised the optimal ratios, showing that many large models were undertrained and that models perform best when parameters and training tokens are scaled to similar magnitudes.

Newer analyses incorporate inference cost, indicating that smaller models trained longer can match the performance of larger models when inference workloads are high. Additional studies focus on how capabilities scale across benchmarks and show that model efficiency increases as architectures, data quality, and training methods improve.

These findings guide model selection and resource planning by emphasizing balanced scaling, adequate training data, and the growing importance of parameter and inference efficiency.

Real-life example: Parallel TTC Scaling with PaCoRe

PaCoRe (Parallel Coordinated Reasoning) is an open-source framework that introduces a new approach to scaling test-time compute (TTC).

Rather than being constrained by a model’s context window, PaCoRe launches massive parallel exploration, then compacts and synthesizes the results via a message-passing architecture, enabling multi-million-token effective compute scaling during inference.

PaCoRe also ships an open server that can be used with arbitrary LLM endpoints, allowing developers apply this parallel scaling approach across different models and providers.9

13. Scaling model size

Increasing the number of parameters in a model means making it larger, typically by adding more layers or making existing layers more complex. Larger models can:

• Capture more complex patterns: With more parameters, the model can represent more intricate relationships in the data.
• Handle larger datasets: Bigger models have greater capacity to process and learn from large-scale data.

However, the relationship between model size and performance may exhibit diminishing returns. A 10x increase in model size does not necessarily lead to a 10x improvement in performance.

Larger models also require exponentially more compute and memory resources, which can make them costly and harder to train. Beyond a certain point, increasing model size might produce negligible gains, particularly if the dataset or compute resources are insufficient.

14. Scaling data

The availability and size of the dataset used to train a model significantly affect its performance:

• Larger datasets improve generalization: With more diverse and comprehensive data, the model learns a wider range of patterns and is less likely to overfit.
• Better understanding of rare events: Large datasets help the model learn rare and diverse patterns, which would make it better at handling unusual cases.

However, scaling data also has limits:

• Leveling off gains: After a certain point, adding more data provides diminishing returns in performance because the model has learned most of the useful patterns.
• Quality over quantity: Poor-quality or noisy data may not improve performance, even in large volumes.
• Compute bottleneck: Larger datasets demand more compute power and training time, which can be prohibitive.

15. Retrieval-augmented generation (RAG)

Retrieval-augmented generation has become an essential strategy for enhancing AI models without relying solely on larger models or increased compute resources. RAG systems integrate a large language model with an external knowledge base, enabling the model to access relevant information in real-time.

Key advantages include:

• Reducing the need for retraining models when new information is created.
• Improving performance on specialized business functions by grounding outputs in curated data sources.
• Mitigating risks of outdated or hallucinated responses by enabling systems to cite background sources.

This approach is now common in enterprise AI solutions, where training data cannot keep pace with rapidly changing domains, such as finance, law, or customer service.

16. Memory-augmented systems

Memory-augmented systems give models access to persistent or session-level memory. This enables the model to maintain context across tasks and interactions.

Important characteristics include:

• Support for long-term context that is not limited by prompt length.
• Improved consistency across multi-step workflows.
• Better alignment with use cases that require continuity, such as project work or complex analysis.

17. Scaling compute

Scaling compute involves increasing the computational power available during training or inference, typically through:

• More powerful hardware: GPUs, TPUs, or specialized AI chips.
• Distributed systems: Training across multiple machines in parallel to handle large workloads.
• Longer training durations: Allowing the model to optimize its weights over more iterations.

The relationship between compute and model performance is foundational:

• More compute enables larger models: Scaling compute allows for training models with more parameters.
• Extended training: With sufficient compute, models can train on larger datasets for longer periods, which would lead to better optimization.

However, scaling compute also has challenges:

• Diminishing returns: While performance improves with more compute, the rate of improvement slows as the resources increase.
• Cost and energy demands: Training advanced models like GPT-4 requires extensive financial and environmental resources.

Despite these challenges, scaling compute has been instrumental in driving AI machine learning improvements.

In the inference stage, the performance of an AI model, particularly for tasks requiring maths or multi-step reasoning, can improve by allocating more compute time. This is often achieved through strategies like increased computation per query or iterative refinement. Here’s how it works:

What happens during inference?

Inference is the stage where a pre-trained model is used to generate predictions or perform tasks based on new inputs. Unlike training, inference doesn’t update the model’s weights but relies on its learned capabilities to solve specific problems.

Why does more computing time help?

When performing tasks like mathematical calculations or multi-step reasoning, the model benefits from more time and resources per query because:

• Iterative refinement: For tasks requiring multiple logical steps, the model can break the problem into smaller parts, solve each part, and iteratively refine its solution. Allocating more compute allows the model to process these steps more thoroughly.
• Increased precision: In mathematical tasks, longer inference time allows for deeper exploration of patterns or trial-and-error mechanisms to approximate correct solutions.
• Better contextual understanding: In tasks like multi-step reasoning, a model with more compute time can evaluate the context repeatedly, to ensure that intermediate steps align with the broader problem.

18. Inference-time compute scaling

Inference-time compute scaling refers to allocating more computation to a model during inference. This approach supports longer reasoning traces and multi-step evaluation without modifying the model’s parameters.

Key points include:

• Models can iteratively refine intermediate steps for tasks that require reasoning.
• Accuracy increases when the model is allowed to run deeper inference paths.
• Performance gains are achieved without retraining, which makes this method suitable for frequent updates.

Real-life example: Post-training and inference-time capability gains

Anthropic’s Claude Opus 4.6 illustrates how frontier AI systems are advancing through improvements in inference-time reasoning and tool integration. These gains show up in more capable agentic coding, where the model can plan multi-step software tasks, navigate large codebases, and iteratively fix its own errors.

They also appear in stronger tool use and coordinated agent workflows, such as agent teams in Claude Code that divide and execute complex tasks.

In addition, Opus 4.6 supports long context windows (up to ~1 million tokens in beta), allowing it to maintain coherence across extended documents, codebases, and multi-step interactions.

Together, these developments highlight how system design and inference-time techniques are driving meaningful capability gains beyond base training alone.

Figure 2: Graph showing Opus 4.6’s performance on Terminal Bench. Terminal Bench is a benchmarking suite for evaluating AI agents operating in terminal environments.10

Real-life example: Gemini 3 Deep Think

Google’s Gemini 3 Deep Think is designed to tackle complex scientific, mathematical, and engineering problems with deeper inferential search and multi-hypothesis exploration.

Deep Think improves performance by changing how the model reasons at inference time, allocating more compute to harder problems rather than relying solely on a larger parameter count.

This shows that reasoning modalities, in which a model can switch to a deep-thinking mode optimized for harder analytical tasks, are emerging as a distinct concept of AI progress alongside parameter count and tooling/deployment improvements.

Figure 3: Graph showing Deep Think’s performance on ARC-AGI 2, Humanity’s Last Exam, MMMU-Pro, and Codeforces benchmarks.11

Real-life example: GPT-5.3-Codex-Spark

OpenAI’s GPT-5.3-Codex-Spark is a coding-focused model positioned as a speed-optimized variant of GPT-5.3-Codex, intended for real-time developer workflows.

Key features include:

• High-throughput inference: Designed for low-latency coding assistance, with output speeds reported at over 1,000 tokens per second in supported environments.
• Large context window: Supports up to 128,000 tokens of context, enabling use with larger codebases and longer sessions.
• Interactive coding workflows: Targeted at iterative coding tasks such as editing, debugging, and code refinement in real time.
• Infrastructure emphasis: Built to run on low-latency inference infrastructure, including deployments on Cerebras hardware.

Figure 4: OpenAI’s GPT-5.3-Codex-Spark benchmark performance on SWE-Bench Pro.12

19. Agentic AI

Instead of relying on a single larger model, agentic systems use different models with defined roles, such as planning, reasoning, and execution.

Advantages include:

• Scaling reasoning capabilities without endlessly increasing parameter counts.
• Greater flexibility in tool use by assigning tasks to the most capable model.
• More straightforward incorporation of feedback from users and stakeholders at different stages of a process.

One example is a multi-agent system where one model handles project management tasks, another interprets natural language inputs, and a third manages data retrieval and integration. Together, these models deliver better results than a single model working alone.

20. Model efficiency techniques

In response to the cost and environmental impact of training larger models, efficiency techniques have recently become a focus. These methods allow developers to improve performance while using fewer resources:

• Quantization reduces the memory footprint by lowering the precision of model parameters without losing quality in predictions.
• Knowledge distillation transfers capabilities from a large model into a smaller model, enabling faster inference.
• Pruning removes redundant parameters to reduce complexity while maintaining accuracy.
• Low-rank adaptation (LoRA) enables efficient fine-tuning of large models on domain-specific tasks with limited resources.

These techniques enable AI systems to be more scalable across various models and business contexts, enabling better results at a lower cost.

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Recommendations on how to approach AI/ML model improvement

Improving an AI/ML model requires a strategic approach to identify areas to implement effective solutions. By combining performance monitoring with hypothesis-driven decision-making, AI/ML models can be refined and optimized for better outcomes:

Monitor performance

You can improve something by knowing its areas for improvement. This can be done by monitoring the features of the AI/ML model. However, if all the model features can not be monitored, a selected number of key features can be observed to study variations in their output that can impact the model’s performance.

Hypothesis generation

Prior to selecting the right method, we recommend performing hypothesis generation. This is a pre-decisional process that structures the decision process and narrows down the options.

This process involves gaining domain knowledge, studying the problem the AI/ML model is facing, and narrowing down readily available options that can tackle the identified issues.

Iterative improvement and experimentation

AI/ML model improvement is an ongoing process. After forming hypotheses and selecting potential solutions, experimentation and iteration are key to refining the model.

A/B Testing: Test different models or changes on subsets of data to compare results. This helps identify which improvements are most effective.

Model retraining: Regularly retrain the model with new data, feature updates, or algorithm adjustments to ensure it stays relevant and adapts to changing conditions.

Automated monitoring and feedback loops: Use automated systems to provide continuous AI feedback, enabling quick adjustments and rapid iteration on improvements.

Incorporate feedback from stakeholders

An often overlooked part of the model improvement process is gathering input from end-users or stakeholders. AI feedback collected from business teams, domain experts, or end users offers valuable context to refine predictions and address real-world blind spots.

Integrating this feedback loop helps ensure the model adapts continuously and remains aligned with operational needs..

This feedback loop ensures the model remains aligned with real-world needs and expectations.

Prioritize the most impactful changes

Not all improvements will have the same level of impact. It is essential to prioritize changes that directly address the most critical performance issues.

For example, improving data quality or addressing a significant bias in the model might have more substantial effects than minor adjustments to the algorithm’s hyperparameters.

Document and standardize the improvement process

For continuous improvements, document the methods, experiments, and results.

Standardizing this process allows for future enhancements to follow a proven, structured approach, ensuring that improvements can be measured, compared, and tracked.

FAQs

The evolution of artificial intelligence has led to remarkable progress in natural language processing (NLP). Today’s AI systems can understand, interpret, and generate human language with unprecedented accuracy. This significant leap is evident in sophisticated chatbots, language translation services, and voice-activated assistants.

To enhance your AI model’s accuracy, consider collecting more high-quality and diverse training data. Additionally, fine-tune your model’s hyperparameters, experiment with different algorithms, and apply techniques like cross-validation to optimize performance.

Prevent AI overfitting by using regularization techniques, implementing dropout layers in neural networks, and employing early stopping during training. Increasing your dataset size and ensuring data diversity can also help your model generalize better to new inputs.

Cite this research

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Cem Dilmegani and Sıla Ermut (2026) - "20 Strategies for AI Improvement & Examples". Published online at AIMultiple.com. Retrieved February 20, 2026, from: https://aimultiple.com/ai-improvement [Online Resource]

Dilmegani, C., & Ermut, S. (2026, February 20). 20 Strategies for AI Improvement & Examples. AIMultiple. https://aimultiple.com/ai-improvement

@misc{dilmegani2026, author = {Dilmegani, Cem and Ermut, Sıla}, title = {{20 Strategies for AI Improvement & Examples}}, year = {2026}, month = feb, howpublished = {\url{https://aimultiple.com/ai-improvement}}, note = {AIMultiple. Retrieved February 20, 2026} }

Cem Dilmegani

Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Researched by

Sıla Ermut

Industry Analyst

Sıla Ermut is an industry analyst at AIMultiple focused on email marketing and sales videos. She previously worked as a recruiter in project management and consulting firms. Sıla holds a Master of Science degree in Social Psychology and a Bachelor of Arts degree in International Relations.

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