How task decomposition and smaller LLMs can make AI more affordable

“Agentic workflows” that use multiple, fine-tuned smaller LLMs — rather than one large one — can improve efficiency.

The expanding use of generative-AI applications has increased the demand for accurate, cost-effective large language models (LLMs). LLMs’ costs vary significantly based on their size, typically measured by the number of parameters: switching to the next smaller size often results in a 70%–90% cost savings. However, simply using smaller, lighter-weight LLMs is not always a viable option due to their diminished capabilities compared to state-of-the-art "frontier LLMs."

Related content
Dependency graphs of business processes with constrained decoding can reduce API hallucinations and out-of-order executions.

While reduction in parameter size usually diminishes performance, evidence suggests that smaller LLMs, when specialized to perform tasks like question-answering or text summarization, can match the performance of larger, unmodified frontier LLMs on those same tasks. This opens the possibility of balancing cost and performance by breaking complex tasks into smaller, manageable subtasks. Such task decomposition enables the use of cost-effective, smaller, more-specialized task- or domain-adapted LLMs while providing control, increasing troubleshooting capability, and potentially reducing hallucinations.

However, this approach comes with trade-offs: while it can lead to significant cost savings, it also increases system complexity, potentially offsetting some of the initial benefits. This blog post explores the balance between cost, performance, and system complexity in task decomposition for LLMs.

As an example, we'll consider the case of using task decomposition to generate a personalized website, demonstrating potential cost savings and performance gains. However, we'll also highlight the potential pitfalls of overengineering, where excessive decomposition can lead to diminishing returns or even undermine the intended benefits.

I. Task decomposition

Ideally, a task would be decomposed into subtasks that are independent of each other. That allows for the creation of targeted prompts and contexts for each subtask, which makes troubleshooting easier by isolating failures to specific subtasks, rather than requiring analysis of a single, large, black-box process.

Related content
“Best-fit packing” adapts bin-packing to avoid unnecessary truncation of training documents, improving LLM performance across a wide range of tasks and reducing hallucination.

Sometimes, however, decomposition into independent subtasks isn’t possible. In those cases, prompt engineering or information retrieval may be necessary to ensure coherence between subtasks. However, overengineering should be avoided, as it can unnecessarily complicate workflows. It also runs the risk of sacrificing the novelty and contextual richness that LLMs can provide by capturing hidden relationships within the complete context of the original task.

But we’ll address these points later. First, let us provide an example where the task of personalized website generation is decomposed into an agentic workflow. The agents in an agentic workflow might be functional agents, which perform specific tasks (e.g., database query), or persona-based agents that mimic human roles in an organization (e.g., UX designer). In this post, I'll focus on the persona-based approach.

A simple example: Creating a personalized website

In our scenario, a business wants to create a website builder that generates tailored web experiences for individual visitors, without human supervision. Generative AI's creativity and ability to work under uncertainty make it suitable for this task. However, it is crucial to control the workflow, ensuring adherence to company policies, best practices, and design guidelines and managing cost and performance.

Generated web pages.png
Examples of web pages produced with generative AI.

This example is based on an agentic-workflow solution we published on the Amazon Web Services (AWS) Machine Learning Blog. For that solution, we divided the overall process into subtasks of a type ordinarily assigned to human agents, such as the personalizer (UX/UI designer/product manager), artist (visual-art creator), and website builder (front-end developer).

LLM decomposition.png
Generating a personalized website using a single large LLM (top) versus decomposing the task using smaller LLMs (bottom).

The personalizer agent aims to provide tailored experiences for website visitors by considering both their profiles and the company's policies, offerings, and design approaches. This is an average-sized text-to-text LLM with some reasoning skills. The agent also incorporates retrieval-augmented generation (RAG) to leverage vetted "company research".

Here’s a sample prompt for the personalizer:

You are an AI UI/UX designer tasked with creating a visually appealing website. Keep in mind the industry pain points [specify relevant pain points — RAG retrieved] to ensure a tailored experience for your customer [provide customer profile — JSON to natural language]. In your response, provide two sections: a website description for front-end developers and visual elements for the artists to follow. You should follow the design guidelines [include relevant design guidelines].

Related content
The fight against hallucination in retrieval-augmented-generation models starts with a method for accurately assessing it.

The artist agent's role is to reflect the visual-elements description in a well-defined image, whether it's a background image or an icon. Text-to-image prompts are more straightforward, starting with "Create an [extracted from personalizer response]."

The final agent is the front-end developer, whose sole responsibility is to create the front-end website artifacts. Here, you can include your design systems, code snippets, or other relevant information. In our simple case, we used this prompt:

You are an experienced front-end web developer tasked with creating an accessible, [specify the website's purpose] website while adhering to the specified guidelines [include relevant guidelines]. Carefully read the 'Website Description' [response from personalizer] provided by the UI/UX designer AI and generate the required HTML, CSS, and JavaScript code to build the described website. Ensure that [include specific requirements].

Here, you can continue the approach with a quality assurance (QA) agent or perform a final pass to see if there are discrepancies.

II. The big trade-off and the trap of overengineering

Task decomposition typically introduces additional components (new LLMs, orchestrators), increasing complexity and adding overhead. While smaller LLMs may offer faster performance, the increased complexity can lead to higher latency. Thus, task decomposition should be evaluated within the broader context.

Let's represent the task complexity as O(n), where n is the task size. With a single LLM, complexity grows linearly with task size. On the other hand, in parallel task decomposition with k subtasks and k smaller language models, the initial decomposition has a constant complexity — O(1). Each of the k language models processes its assigned subtask independently, with a complexity of O(n/k), assuming an even distribution.

Related content
Automated method that uses gradients to identify salient layers prevents regression on previously seen data.

After processing, the results from the k language models need coordination and integration. This step's complexity is O(km), where fully pairwise coordination gives m = 2, but in reality, 1 < m ≤ 2.

Therefore, the overall complexity of using multiple language models with task decomposition can be expressed as

Ok-LLMs = O(1) + k (O(n/k)) + O(km) O(n) + O(km)

While the single-language-model approach has a complexity of O(n), the multiple-language-model approach introduces an additional term, O(km), due to coordination and integration overhead, with 1 < m ≤ 2.

For small k values and pairwise connectivity, the O(km) overhead is negligible compared to O(n), indicating the potential benefit of the multiple-language-model approach. However, as k and m grow, the O(km) overhead becomes significant, potentially diminishing the gains of task decomposition. The optimal approach depends on the task, the available resources, and the trade-off between performance gains and coordination overhead. Improving technologies will reduce m, lowering the complexity of using multiple LLMs.

A mental model for cost and complexity

A helpful mental model for deciding whether to use task decomposition is to consider the estimated total cost of ownership (TCO) of your application. As your user base grows, infrastructure cost becomes dominant, and optimization methods like task decomposition can reduce TCO, despite the upfront engineering and science costs. For smaller applications, a simpler approach, such as selecting a large model, may be more appropriate and cost effective.

Mental model.png
A mental model to help decide the question of complexity versus simplicity.

Overengineering versus novelty and simplicity

Task decomposition and the creation of agentic workflows with smaller LLMs can come at the cost of the novelty and creativity that larger, more powerful models often display. By “manually” breaking tasks into subtasks and relying on specialized models, the overall system may fail to capture the serendipitous connections and novel insights that can emerge from a more holistic approach. Additionally, the process of crafting intricate prompts to fit specific subtasks can result in overly complex and convoluted prompts, which may contribute to reduced accuracy and increased hallucinations.

Task decomposition using multiple, smaller, fine-tuned LLMs offers a promising approach to improving cost efficiency for complex AI applications, potentially providing substantial infrastructure cost savings compared to using a single, large, frontier model. However, care must be taken to avoid overengineering, as excessive decomposition can increase complexity and coordination overhead to the point of diminishing returns. Striking the right balance between cost, performance, simplicity, and retaining AI creativity will be key to unlocking the full potential of this promising approach.

Related content

GB, MLN, Edinburgh
We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office experienced in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization. You will lead the design of machine learning models that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science team to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization. Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide. Key job responsibilities Develop machine learning algorithms for high-scale recommendations problems. Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement. Collaborate with software engineers to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.
IN, TS, Hyderabad
Welcome to the Worldwide Returns & ReCommerce team (WWR&R) at Amazon.com. WWR&R is an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, our company, and the environment. Our goal is to achieve the three zeroes: zero cost of returns, zero waste, and zero defects. We do this by developing products and driving truly innovative operational excellence to help customers keep what they buy, recover returned and damaged product value, keep thousands of tons of waste from landfills, and create the best customer returns experience in the world. We have an eye to the future – we create long-term value at Amazon by focusing not just on the bottom line, but on the planet. We are building the most sustainable re-use channel we can by driving multiple aspects of the Circular Economy for Amazon – Returns & ReCommerce. Amazon WWR&R is comprised of business, product, operational, program, software engineering and data teams that manage the life of a returned or damaged product from a customer to the warehouse and on to its next best use. Our work is broad and deep: we train machine learning models to automate routing and find signals to optimize re-use; we invent new channels to give products a second life; we develop highly respected product support to help customers love what they buy; we pilot smarter product evaluations; we work from the customer backward to find ways to make the return experience remarkably delightful and easy; and we do it all while scrutinizing our business with laser focus. You will help create everything from customer-facing and vendor-facing websites to the internal software and tools behind the reverse-logistics process. You can develop scalable, high-availability solutions to solve complex and broad business problems. We are a group that has fun at work while driving incredible customer, business, and environmental impact. We are backed by a strong leadership group dedicated to operational excellence that empowers a reasonable work-life balance. As an established, experienced team, we offer the scope and support needed for substantial career growth. Amazon is earth’s most customer-centric company and through WWR&R, the earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns & ReCommerce team!
US, WA, Bellevue
mmPROS Surface Research Science seeks an exceptional Applied Scientist with expertise in optimization and machine learning to optimize Amazon's middle mile transportation network, the backbone of its logistics operations. Amazon's middle mile transportation network utilizes a fleet of semi-trucks, trains, and airplanes to transport millions of packages and other freight between warehouses, vendor facilities, and customers, on time and at low cost. The Surface Research Science team delivers innovation, models, algorithms, and other scientific solutions to efficiently plan and operate the middle mile surface (truck and rail) transportation network. The team focuses on large-scale problems in vehicle route planning, capacity procurement, network design, forecasting, and equipment re-balancing. Your role will be to build innovative optimization and machine learning models to improve driver routing and procurement efficiency. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers. You will operate as part of a team of innovative, experienced scientists working on optimization and machine learning. You will work in close collaboration with partners across product, engineering, business intelligence, and operations. Key job responsibilities - Design and develop optimization and machine learning models to inform our hardest planning decisions. - Implement models and algorithms in Amazon's production software. - Lead and partner with product, engineering, and operations teams to drive modeling and technical design for complex business problems. - Lead complex modeling and data analyses to aid management in making key business decisions and set new policies. - Write documentation for scientific and business audiences. About the team This role is part of mmPROS Surface Research Science. Our mission is to build the most efficient and optimal transportation network on the planet, using our science and technology as our biggest advantage. We leverage technologies in optimization, operations research, and machine learning to grow our businesses and solve Amazon's unique logistical challenges. Scientists in the team work in close collaboration with each other and with partners across product, software engineering, business intelligence, and operations. They regularly interact with software engineering teams and business leadership.
US, WA, Seattle
Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for talented Applied Scientists to join the team. Key job responsibilities As a Applied Scientist II, you will: * Conduct hands-on data analysis, build large-scale machine-learning models and pipelines * Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production * Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving * Provide technical leadership, research new machine learning approaches to drive continued scientific innovation * Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! In Prime Video READI, our mission is to automate infrastructure scaling and operational readiness. We are growing a team specialized in time series modeling, forecasting, and release safety. This team will invent and develop algorithms for forecasting multi-dimensional related time series. The team will develop forecasts on key business dimensions with optimization recommendations related to performance and efficiency opportunities across our global software environment. As a founding member of the core team, you will apply your deep coding, modeling and statistical knowledge to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on retrieving, cleansing and preparing large scale datasets, training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with complete independence and are often assigned to focus on areas where the business and/or architectural strategy has not yet been defined. You must be equally comfortable digging in to business requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than delivering for our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies to your solutions. If you crave a sense of ownership, this is the place to be.
IL, Tel Aviv
Come join the AWS Agentic AI science team in building the next generation models for intelligent automation. AWS, the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making. Key job responsibilities PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience 3+ years of building models for business application experience Experience in patents or publications at top-tier peer-reviewed conferences or journals Experience programming in Java, C++, Python or related language Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
IL, Haifa
Come join the AWS Agentic AI science team in building the next generation models for intelligent automation. AWS, the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making.
US, NY, New York
Join us in a historic endeavor to make Generative AI accessible to the world with breakthrough research! The AWS AI team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists drives the innovation that enables external and internal SageMaker customers to train their next generation models on both GPU and Trainium instances. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. About the team Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Utility Computing (UC) AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (IoT), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities As an Applied Scientist in the Content Understanding Team, you will lead the end-to-end research and deployment of video and multi-modal models applied to a variety of downstream applications. More specifically, you will: - Work backwards from customer problems to research and design scientific approaches for solving them - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals About the team Our Prime Video Content Understanding team builds holistic media representations (e.g. descriptions of scenes, semantic embeddings) and apply them to new customer experiences supply chain problems. Our technology spans the entire Prime Video catalogue globally, and we enable instant recaps, skip intro timing, ad placement, search, and content moderation.
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities As an Applied Scientist in the Content Understanding Team, you will lead the end-to-end research and deployment of video and multi-modal models applied to a variety of downstream applications. More specifically, you will: - Work backwards from customer problems to research and design scientific approaches for solving them - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals About the team Our Prime Video Content Understanding team builds holistic media representations (e.g. descriptions of scenes, semantic embeddings) and apply them to new customer experiences supply chain problems. Our technology spans the entire Prime Video catalogue globally, and we enable instant recaps, skip intro timing, ad placement, search, and content moderation.