Optimising LLMOps: Improvement Beyond Limits!

If we didn’t have LLMOps, the Internet as it is today simply wouldn’t exist. We live in an era of great automation, where content generation is just two clicks away. How is it that LLMOps are so powerful, though? What technology is behind this success? Let’s find out!

Optimising LLMOps: Improvement Beyond Limits!
Written by TechnoLynx Published on 02 Jan 2025

Introduction

In our previous article about Machine Learning Operations (MLOps) and Large Language Model Operations (LLMOps), we discussed what each is, their similarities, and the differences that characterise them. Focusing on LLMOps, now that the general idea is understood, why don’t we have a look at how they can be improved and optimised based on the application and the tasks we want them to perform?

Evaluation Methods

As with every Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) model, there are certain ways to evaluate the performance of an LLMOp. Starting with the basics, accuracy and precision are very good starting points. Roughly, accuracy measures how often predictions of a model are correct, which is the ratio of the correct predictions over the total number of predictions, while precision is the ratio of the true positive values over the total number of positives (true and false). If this sounds complicated, let us give you a simplified example.

You take 100 people and classify them as diabetic or non-diabetic using ML just by reading their recent blood glucose levels. Let us gather all the results in a table, also known as a ‘confusion matrix’:

Table 1 – An example of a confusion matrix that presents predictions between two classes (‘Diabetic’ and ‘Healthy’) in a population of 100 people.

As you can see, the sum of all categories adds up to 100. Calculating the accuracy and prediction, we find that the accuracy of the model is equal to 36%, while the precision is 53,47%.

Two more advanced but still fundamental evaluation methods are the Recall and F1-score, which are the ratio of true positive results to the actual number of positive cases in the entire dataset and harmonic mean of precision and recall (we will let you do the maths on these. Of course, other factors are also essential. Don’t forget that we are talking about Generative AI (GenAI) after all, so it would be useless without a proper response time. If the response time is not good, it will be as annoying as having lag in an online call. Robustness and reliability are also essential, as they ensure proper function even when the data load is large or an unexpected query has been made. Similarly, proper Resource Utilisation is important, as LLMOps need to generate the best output as fast as possible while keeping the CPU and GPU load low, especially in GPU-accelerated tasks such as AI image generation.

It feels overwhelming, doesn’t it? Keep in mind that these are only the essentials! The true struggle of LLMs is to understand questions that seem pretty essential to us humans, with misinterpretation reaching a difference of 47% (95% in humans and 48% in machines). A true way to evaluate the performance of an LLM is by using tools like HellaSwag. Simply put, HellaSwag presents a model with sentences that make sense and sentences that don’t. The model is presented with the sentences in groups of four, all of which have the same beginning but a different ending. Of these four, only one makes the most sense, which is also labelled as such. The model’s probability of correctly predicting is computed, and if the labelled ending has the highest probability, it is considered a correct prediction. The groups with the correct predictions give us the resulting accuracy (GitHub, n.d.).

Full Steam Ahead!

Transfer learning

So, let’s suppose that you have your LLMOp locked and loaded, ready to fire in a commercial task. After evaluating its performance using the basic methods that we mentioned above, a very smart thing to do is Transfer Learning (TL). Basically, TL takes a developed model or algorithm, which has been tested on a specific topic, and uses it on different data. It might sound counter-intuitive; however, it plays a major role when developing an all-around model. It is no wonder that the GenAI models developed by leading companies such as OpenAI, Microsoft, and Google are so successful. Imagine if they were only able to answer questions related to a single topic. Where is the success in that? By training an LLMOp in different datasets, we can test how versatile our pipeline is and how many different applications it can have (Amazon Web Services, Inc., n.d.c). You don’t believe us? What, you think all Computer Vision (CV) algorithms have the same capabilities regardless of the application? The training between airport security cameras is way different than the training for Augmented Reality (AR) or Extended Reality (XR) goggles that you use to play your favourite games!

Finetuning

TL is more or less a generalisation of the applications an LLMOp model can have. But what about precision and specificity? You can have a functioning model; however, that does not mean that it provides 100% accurate content. One of the most famous NLPs (take a guess) had actually been proven to have such an issue during its early stages. When asked to provide web sources for the content it generated, the results were a mess. Links were leading to non-existent pages, or they were not working at all. The way to fix that is Finetuning, and even though, in theory, it doesn’t sound that difficult, it really makes a difference! Examples of finetuning include:

  • Pre-trained Model Selection, where a specific model is selected based on performance on related tasks and compatibility with the task’s requirements.

  • Hyperparameter tuning, such as the learning rate band atch size.

  • Task-specific adaptation, where the pre-trained model is modified by adding task-specific layers or adjusting the existing layers to better align with the target task.

  • Training with task-specific data, which involves feeding the pre-trained model with data specific to the target task. This allows the model to learn task-specific patterns and nuances that were not explicitly covered in its pre-training phase (Shanepeckham, 2024).

Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) is probably one of the coolest ways to boost the performance of an NLP. As you know, LLMOps are trained on massive datasets to provide accurate and ready-for-production content. If one thing is true, it is that a dataset can never be enough to train a machine. Here is where RAG comes in to make the NLP outperform itself each time, and our guess is that you have already used it; you just didn’t know you had (Merritt, 2023)!

Simply put, RAG is the perfect combination between retrieving data and the one the model has been trained on to generate content. RAG is split into three discrete phases. The first phase is called the ‘Retrieval’ phase. During retrieval, the model identifies documents or text from databases that it is already familiar with and is trained in. The second phase is ‘Augmentation’, during which the model uses the retrieved documents or text as an additional source of information, a crucial step in generating more authentic and context-specific content. Last, we have the ‘Generation’ phase, during which the initial query and the augmentation result are combined for the best result (Amazon Web Services, Inc., n.d.b).

RAG is the technology responsible for the consistency of your conversation with your favourite Natural Language Processing (NLP) assistant, as RAG is used to improve the flow and relevance of the dialogue between you and the machine. We said that you probably have used RAG, but you just don’t realise. Roll back to the last time you uploaded a document to your NLP assistant or when you asked it to summarise a text for you!

Figure 1 – Illustration of the three most widely used optimisation methods for LLMOps
Figure 1 – Illustration of the three most widely used optimisation methods for LLMOps

Read more: Understanding Retrieval Augmented Generation (RAG)

LangChain and Model Chaining

LangChain

LangChain is an abbreviation, a combination, if you want, of the words ‘language’ and ‘chain’, and it can have many implementations. It has been established that LLMOps are pipelines on which a Large Language Model (LLM) is trained. Now earlier, we said that a way to ensure a more generic application of an LLMOp is by using TL. As you understand, this would take quite some time, and it can be resourceful, not to mention that the possibility of an error increases if the prompt engineers are not cautious and careful. Of course, there is the space problem, as the hardware for such tasks can sometimes occupy entire rooms! The Internet of Things (IoT) can aid in that, as infrastructure spreads in different areas, it can really make a difference when large spaces are required. And, when an area is remote, Edge Computing can be significant. Is there a reason to face these problems when you can avoid them from the beginning, though?

Instead of solving the unsolved, what can be done is to train different LLMOps in different pipelines and data sets (based on the desired output and ‘specialisation’) and link them together. Then, depending on the context and the prompt, the appropriate pipeline can be used and linked with others (Amazon Web Services, Inc., n.d.a) Simply put, imagine someone being a skilled engineer, electrician, doctor, pilot, plumber, cook, and professional athlete capable of using all of their knowledge at will!

Model Chaining

Similarly to LangChain, Model Chaining provides a more specialised answer or more specific content. While LangChain switches between different pipelines, Model Chaining is based on switching between different models according to the desired task the LLMOp needs to solve. A key element here is the order of execution, as in Model Chaining, each model’s output serves as an input to the next. The big difference, hence, between LangChain and Model Chaining is that, while LangChain optimisation is concurrent, Model Chaining is strictly sequential.

Let’s explain it using an example from everyday life. Pretend that you are cooking a meal. You start by chopping the veggies, then heat the stoves, sear them in a pan, and finally, set the table to serve the dish. Each step depends on the previous one: you can’t cook until the vegetables are chopped, and you can’t really serve until the cooking is done and the table is set. This is a sequential process, where every step happens strictly after the other. Now, imagine you have an assistant in the kitchen. While you’re chopping the vegetables, your helper preheats the oven and sets the table at the same time. These tasks don’t depend on each other, so doing them simultaneously saves time. This is a concurrent process, where independent tasks are being done simultaneously, making the overall process quicker and more efficient.

Figure 2 – Illustration of two approaches that an LLMOp can ‘think’ to generate content
Figure 2 – Illustration of two approaches that an LLMOp can ‘think’ to generate content

Summing Up

In our previous article on MLOps and LLMOps, we discussed the key differences and similarities between the two. After understanding how each works and what principles it is based on, we focused on how to improve the most complex of the two. This doesn’t mean that this is where it stops. In fact, this is only a sample of ways in which LLMOps can be improved. That is, so far, new ways will surely be found while technology advances. One thing is certain. LLMOps are powerful tools, the limits of which are only our skills.

What We Offer

One thing we really know at TechnoLynx is how to innovate. We offer solutions that are custom-tailored for your needs, made on demand, from scratch, and specifically designed for each project. Delivering tech solutions is our specialisation because we truly understand the benefits of AI, dare we say, better than anyone. We are committed to providing cutting-edge solutions in any field, enriching your project with AI solutions while ensuring safety in human-machine interactions. We take pride in managing and analysing large data sets while at the same time addressing ethical considerations.

Our software solutions are precise, empowering many fields and industries using innovative AI-driven algorithms, never resting and always adapting to the ever-changing AI landscape. The solutions we present are designed to increase accuracy, efficiency, and productivity. Feel free to contact us to share your ideas. Let us boost your project!

Continue reading: Understanding Language Models: How They Work

List of references

AI-Driven Opportunities for Smarter Problem Solving

AI-Driven Opportunities for Smarter Problem Solving

5/08/2025

AI-driven problem-solving opens new paths for complex issues. Learn how machine learning and real-time analysis enhance strategies.

10 Applications of Computer Vision in Autonomous Vehicles

10 Applications of Computer Vision in Autonomous Vehicles

4/08/2025

Learn 10 real world applications of computer vision in autonomous vehicles. Discover object detection, deep learning model use, safety features and real time video handling.

How AI Is Transforming Wall Street Fast

How AI Is Transforming Wall Street Fast

1/08/2025

Discover how artificial intelligence and natural language processing with large language models, deep learning, neural networks, and real-time data are reshaping trading, analysis, and decision support on Wall Street.

How AI Transforms Communication: Key Benefits in Action

How AI Transforms Communication: Key Benefits in Action

31/07/2025

How AI transforms communication: body language, eye contact, natural languages. Top benefits explained. TechnoLynx guides real‑time communication with large language models.

Top UX Design Principles for Augmented Reality Development

Top UX Design Principles for Augmented Reality Development

30/07/2025

Learn key augmented reality UX design principles to improve visual design, interaction design, and user experience in AR apps and mobile experiences.

AI Meets Operations Research in Data Analytics

AI Meets Operations Research in Data Analytics

29/07/2025

AI in operations research blends data analytics and computer science to solve problems in supply chain, logistics, and optimisation for smarter, efficient systems.

Generative AI Security Risks and Best Practice Measures

Generative AI Security Risks and Best Practice Measures

28/07/2025

Generative AI security risks explained by TechnoLynx. Covers generative AI model vulnerabilities, mitigation steps, mitigation & best practices, training data risks, customer service use, learned models, and how to secure generative AI tools.

Best Lightweight Vision Models for Real‑World Use

Best Lightweight Vision Models for Real‑World Use

25/07/2025

Discover efficient lightweight computer vision models that balance speed and accuracy for object detection, inventory management, optical character recognition and autonomous vehicles.

Image Recognition: Definition, Algorithms & Uses

Image Recognition: Definition, Algorithms & Uses

24/07/2025

Discover how AI-powered image recognition works, from training data and algorithms to real-world uses in medical imaging, facial recognition, and computer vision applications.

AI in Cloud Computing: Boosting Power and Security

AI in Cloud Computing: Boosting Power and Security

23/07/2025

Discover how artificial intelligence boosts cloud computing while cutting costs and improving cloud security on platforms.

 AI, AR, and Computer Vision in Real Life

AI, AR, and Computer Vision in Real Life

22/07/2025

Learn how computer vision, AI, and AR work together in real-world applications, from assembly lines to social media, using deep learning and object detection.

Real-Time Computer Vision for Live Streaming

Real-Time Computer Vision for Live Streaming

21/07/2025

Understand how real-time computer vision transforms live streaming through object detection, OCR, deep learning models, and fast image processing.

3D Visual Computing in Modern Tech Systems

18/07/2025

Understand how 3D visual computing, 3D printing, and virtual reality transform digital experiences using real-time rendering, computer graphics, and realistic 3D models.

Creating AR Experiences with Computer Vision

17/07/2025

Learn how computer vision and AR combine through deep learning models, image processing, and AI to create real-world applications with real-time video.

Machine Learning and AI in Communication Systems

16/07/2025

Learn how AI and machine learning improve communication. From facial expressions to social media, discover practical applications in modern networks.

The Role of Visual Evidence in Aviation Compliance

15/07/2025

Learn how visual evidence supports audit trails in aviation. Ensure compliance across operations in the United States and stay ahead of aviation standards.

GDPR-Compliant Video Surveillance: Best Practices Today

14/07/2025

Learn best practices for GDPR-compliant video surveillance. Ensure personal data safety, meet EU rules, and protect your video security system.

Next-Gen Chatbots for Immersive Customer Interaction

11/07/2025

Learn how chatbots and immersive portals enhance customer interaction and customer experience in real time across multiple channels for better support.

Real-Time Edge Processing with GPU Acceleration

10/07/2025

Learn how GPU acceleration and mobile hardware enable real-time processing in edge devices, boosting AI and graphics performance at the edge.

AI Visual Computing Simplifies Airworthiness Certification

9/07/2025

Learn how visual computing and AI streamline airworthiness certification. Understand type design, production certificate, and condition for safe flight for airworthy aircraft.

Real-Time Data Analytics for Smarter Flight Paths

8/07/2025

See how real-time data analytics is improving flight paths, reducing emissions, and enhancing data-driven aviation decisions with video conferencing support.

AI-Powered Compliance for Aviation Standards

7/07/2025

Discover how AI streamlines automated aviation compliance with EASA, FAA, and GDPR standards—ensuring data protection, integrity, confidentiality, and aviation data privacy in the EU and United States.

AI Anomaly Detection for RF in Emergency Response

4/07/2025

Learn how AI-driven anomaly detection secures RF communications for real-time emergency response. Discover deep learning, time series data, RF anomaly detection, and satellite communications.

AI-Powered Video Surveillance for Incident Detection

3/07/2025

Learn how AI-powered video surveillance with incident detection, real-time alerts, high-resolution footage, GDPR-compliant CCTV, and cloud storage is reshaping security.

Artificial Intelligence on Air Traffic Control

24/06/2025

Learn how artificial intelligence improves air traffic control with neural network decision support, deep learning, and real-time data processing for safer skies.

5 Ways AI Helps Fuel Efficiency in Aviation

11/06/2025

Learn how AI improves fuel efficiency in aviation. From reducing fuel use to lowering emissions, see 5 real-world use cases helping the industry.

AI in Aviation: Boosting Flight Safety Standards

10/06/2025

Learn how AI is helping improve aviation safety. See how airlines in the United States use AI to monitor flights, predict problems, and support pilots.

IoT Cybersecurity: Safeguarding against Cyber Threats

6/06/2025

Explore how IoT cybersecurity fortifies defences against threats in smart devices, supply chains, and industrial systems using AI and cloud computing.

Large Language Models Transforming Telecommunications

5/06/2025

Discover how large language models are enhancing telecommunications through natural language processing, neural networks, and transformer models.

Real-Time AI and Streaming Data in Telecom

4/06/2025

Discover how real-time AI and streaming data are transforming the telecommunications industry, enabling smarter networks, improved services, and efficient operations.

AI in Aviation Maintenance: Smarter Skies Ahead

3/06/2025

Learn how AI is transforming aviation maintenance. From routine checks to predictive fixes, see how AI supports all types of maintenance activities.

AI-Powered Computer Vision Enhances Airport Safety

2/06/2025

Learn how AI-powered computer vision improves airport safety through object detection, tracking, and real-time analysis, ensuring secure and efficient operations.

Fundamentals of Computer Vision: A Beginner's Guide

30/05/2025

Learn the basics of computer vision, including object detection, convolutional neural networks, and real-time video analysis, and how they apply to real-world problems.

Computer Vision in Smart Video Surveillance powered by AI

29/05/2025

Learn how AI and computer vision improve video surveillance with object detection, real-time tracking, and remote access for enhanced security.

Generative AI Tools in Modern Video Game Creation

28/05/2025

Learn how generative AI, machine learning models, and neural networks transform content creation in video game development through real-time image generation, fine-tuning, and large language models.

Artificial Intelligence in Supply Chain Management

27/05/2025

Learn how artificial intelligence transforms supply chain management with real-time insights, cost reduction, and improved customer service.

Content-based image retrieval with Computer Vision

26/05/2025

Learn how content-based image retrieval uses computer vision, deep learning models, and feature extraction to find similar images in vast digital collections.

What is Feature Extraction for Computer Vision?

23/05/2025

Discover how feature extraction and image processing power computer vision tasks—from medical imaging and driving cars to social media filters and object tracking.

Machine Vision vs Computer Vision: Key Differences

22/05/2025

Learn the differences between machine vision and computer vision—hardware, software, and applications in automation, autonomous vehicles, and more.

Computer Vision in Self-Driving Cars: Key Applications

21/05/2025

Discover how computer vision and deep learning power self-driving cars—object detection, tracking, traffic sign recognition, and more.

Machine Learning and AI in Modern Computer Science

20/05/2025

Discover how computer science drives artificial intelligence and machine learning—from neural networks to NLP, computer vision, and real-world applications. Learn how TechnoLynx can guide your AI journey.

Real-Time Data Streaming with AI

19/05/2025

You have surely heard that ‘Information is the most powerful weapon’. However, is a weapon really that powerful if it does not arrive on time? Explore how real-time streaming powers Generative AI across industries, from live image generation to fraud detection.

Core Computer Vision Algorithms and Their Uses

17/05/2025

Discover the main computer vision algorithms that power autonomous vehicles, medical imaging, and real-time video. Learn how convolutional neural networks and OCR shape modern AI.

Applying Machine Learning in Computer Vision Systems

14/05/2025

Learn how machine learning transforms computer vision—from object detection and medical imaging to autonomous vehicles and image recognition.

Cutting-Edge Marketing with Generative AI Tools

13/05/2025

Learn how generative AI transforms marketing strategies—from text-based content and image generation to social media and SEO. Boost your bottom line with TechnoLynx expertise.

AI Object Tracking Solutions: Intelligent Automation

12/05/2025

AI tracking solutions are incorporating industries in different sectors in safety, autonomous detection and sorting processes. The use of computer vision and high-end computing is key in AI tracking.

Feature Extraction and Image Processing for Computer Vision

9/05/2025

Learn how feature extraction and image processing enhance computer vision. Discover techniques, applications, and how TechnoLynx can assist your AI projects.

Fine-Tuning Generative AI Models for Better Performance

8/05/2025

Understand how fine-tuning improves generative AI. From large language models to neural networks, TechnoLynx offers advanced solutions for real-world AI applications.

← Back to Blog Overview