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

IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
IN, HR, Gurugram
We're on a journey to build something new a green field project! Come join our team and build new discovery and shopping products that connect customers with their vehicle of choice. We're looking for a talented Senior Applied Scientist to join our team of product managers, designers, and engineers to design, and build innovative automotive-shopping experiences for our customers. This is a great opportunity for an experienced engineer to design and implement the technology for a new Amazon business. We are looking for a Applied Scientist to design, implement and deliver end-to-end solutions. We are seeking passionate, hands-on, experienced and seasoned Senior Applied Scientist who will be deep in code and algorithms; who are technically strong in building scalable computer vision machine learning systems across item understanding, pose estimation, class imbalanced classifiers, identification and segmentation.. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and dynamic learning. As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on complex customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a unified team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a bold disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities Architect, design, and implement Machine Learning models for vision systems on robotic platforms Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap. Work with stakeholders across , science, and operations teams to iterate on design and implementation. Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment Work directly with customers and partners to test prototypes and incorporate feedback Mentor other engineer team members. A day in the life - 6+ years of building machine learning models for retail application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning - Demonstrated expertise in computer vision and machine learning techniques.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field or relevant science experience (publications/scientific prototypes) in lieu of Masters - Experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment - Papers published in AI/ML venues of repute
US, WA, Bellevue
Our mission is to provide consistent, accurate, and relevant delivery information to every single page on every Amazon-owned site. MILLIONS of customers will be impacted by your contributions: The changes we make directly impact the customer experience on every Amazon site. This is a great position for someone who likes to leverage Machine learning technologies to solve the real customer problems, and also wants to see and measure their direct impact on customers. We are a cross-functional team that owns the ENTIRE delivery experience for customers: From the business requirements to the technical systems that allow us to directly affect the on-site experience from a central service, business and technical team members are integrated so everyone is involved through the entire development process. You will have a chance to develop the state-of-art machine learning, including deep learning and reinforcement learning models, to build targeting, recommendation, and optimization services to impact millions of Amazon customers. Do you want to join an innovative team of scientists and engineers who use machine learning and statistical techniques to deliver the best delivery experience on every Amazon-owned site? Are you excited by the prospect of analyzing and modeling terabytes of data on the cloud and create state-of-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the DEX AI team. Key job responsibilities - Research and implement machine learning techniques to create scalable and effective models in Delivery Experience (DEX) systems - Solve business problems and identify business opportunities to provide the best delivery experience on all Amazon-owned sites. - Design and develop highly innovative machine learning and deep learning models for big data. - Build state-of-art ranking and recommendations models and apply to Amazon search engine. - Analyze and understand large amounts of Amazon’s historical business data to detect patterns, to analyze trends and to identify correlations and causalities - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for 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. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
US, WA, Seattle
The Amazon Photos team is looking for a world-class Applied Scientist to join us and use AI to help customers relive their cherished memories. Our team of scientists have developed algorithms and models that power Amazon Photos features for millions of photos and videos daily. As part of the team, we expect that you will develop innovative solutions to hard problems at massive scale, and publish your findings in at peer reviewed conferences and workshops. With all the recent advancements in Vision-Language models, Amazon Photos has completely re-thought the product roadmap and is looking for Applied Scientists to deliver both the short-term roadmap working closely with Product and Engineering and make investments for the long-term. Our research themes include, but are not limited to: foundational models, contrastive learning, diffusion models, few-shot and zero-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, deep learning, and large scale image and video detection and recognition. Key job responsibilities - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in Visual-Language Model space - Design and execute experiments to evaluate the performance of different models, and iterate quickly to improve results - Create robust evaluation frameworks for assessing model performance across different domains and use cases - Think big about the Visual-Language Model space over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems within Amazon Photos - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports About the team Amazon Photos is the one of the main digital products offered to Prime subscribers along with Amazon Music and Amazon Video. Amazon Photos provides unlimited photo storage and 5 GB for videos to Prime members and is a top Prime benefit in multiple marketplaces. AI-driven experiences based on image and video understanding are core to customer delight for the business. These experiences are delivered in our mobile, web and desktop apps, in Fire TV, and integrated into Alexa devices such as Echo Show. We solve real-world problems using AI while being a positive force for good.