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August 23, 2023

LLM Implementation Strategy: Preparation Guide for Using LLMs


Artur Haponik

CEO & Co-Founder

Reading time:

14 minutes

This guide will discuss LLMs in their entirety and provide a roadmap toward the successful implementation of LLMs across the organization.

The world has come a long way since the development of first-generation computers in the 1940s and 50s. [1] In a decade, organizations considerably reduced their reliance on human resources to make calculations and organize records. However, these computers were still very slow and could only perform limited operations. Fast forward about 70 years later, and computers have taken over everything. With a simple portable computer, you can run complex programs and automate multiple processes.

2017 saw yet another dramatic development in computational capabilities with the realization of the potential contributions of transformer-based models in revolutionizing NLP.[2] NLP then gave rise to more powerful AI models – Large Language Models (LLMs). And now, the potential of LLMs to streamline processes has organizations rushing to implement these complex systems into their workflows. So much so that the global market value of LLMs is projected to reach upwards of $51.8 billion by 2028, up from $11.3 billion in 2023. [3]


Introduction of Large Language Models (LLMs)

Large Language Models (LLMs) are some of the most advanced forms of Artificial Intelligence (AI) that are taking the market by storm. They can perform a wide variety of language-related tasks, making them a valuable resource for any organization that needs to process vast amounts of text data.

In a nutshell, LLMs are advanced language models trained on a massive amount of text data using deep learning techniques. Data scientists and software developers often use unlabeled training data to create a self or semi-supervised learning methodology.

Essentially, the model ‘ingests’ vast amounts of text data, learns the relationships between the data, and attempts to predict the next word in a sentence. The models also use backpropagation techniques to adjust their parameters based on prediction errors.

After training, LLM models are fine-tuned using task-specific data to improve performance in their intended areas of applications. Data scientists also adjust the models’ parameters to further improve their performance.

When it comes to LLM models like ChatGPT, a higher number of parameters usually indicates better performance and accuracy.

LLM general architecture

LLMs use the same general architecture to perform different operations regardless of their size. The architecture is typically divided into several layers that work together to process input data and generate output predictions. These layers include:

LLM architecture

Input embeddings

The embedding layer tokenizes input into smaller units, such as sub-words, and embeds each token into a continuous vector representation. These embeddings capture the syntactic and semantic information about the words in the input text to help the model better understand their context. [4]

Positional encoding

As transformer-based models, LLMs can’t naturally encode the order of tokens. Therefore, this layer adds positional encoding to input embeddings, thus enabling the model to process tokens in their sequential order. [5]


The encoder utilizes neural network techniques to analyze input text and create several hidden states that protect the text data’s context and meaning. The encoder layer is made up of two sub-components. [6]

They include:

  • Self-attention mechanism: The purpose of the self-attention mechanism is to allow the model to consider the relationships and dependencies of different tokens in a context-aware manner. The mechanism achieves this by enabling the model to weigh the importance of tokens in the input text by computing attention scores.
  • Feed-forward neural network: From the self-attention mechanism, each token goes through the feed-forward neural network independently. The network comprises fully connected layers with nonlinear activation functions, allowing the model to capture complex interactions between tokens.


Some transformer models also feature an additional decoder component on top of the encoder. The primary role of the decoder is to enable autoregressive generation. This way, the model is better able to generate sequential outputs by referencing previously generated tokens.

Multi-head attention

Multi-head attention comes in handy in situations where the model performs self-attention with differently learned attention weights. Utilizing this layer allows the model to attend to various parts of the sequence and simultaneously capture different types of relationships.

Layer normalization

Transformer models apply layer normalization after each layer in the architecture. This action primarily aims to stabilize the model’s learning process and ability to generalize across different outputs.

Output layers

Transformer models utilize different output layers depending on the task. For instance, in language modeling, models typically apply a linear projection followed by SoftMax activation to generate the probability distribution for the next token.

Examples of LLMs

The past five years can only be described as an ‘AI arms race.’ Organizations, startups, and venture capitalists have invested billions of dollars in the development of LLMs. The result has been an upsurge in the number of LLM models available.

Here are some of the most popular LLMs today.

  • GPT-3: The Generative Pre-Trained Transformer 3 (GPT-3) is the third generation in the GPT series. The language model developed by OpenAI is also one of the largest language models in the world, with more than 175 billion parameters. This enables it to perform a wide variety of tasks, including text generation, summarization, and translation. The company has also released the fourth-generation model of the series, GPT-4, which is much larger than its predecessors, with 1.7 billion parameters.
  • BERT: BERT, short for Bidirectional Encoder Representations from Transformers, is an LLM developed by Google. The model was trained on a massive amount of data and can understand the context of a sentence and generate meaningful responses to any question.
  • RoBERTa: Robustly Optimized BERT Pre-Training Approach (RoBERTa) is a refined, improved version of BERT developed by Facebook AI Research to perform better than its predecessor on language-related tasks.
  • XLNeT: XLNeT takes a different approach to language modeling. Unlike other language models on our list, this LLM developed by Google and Carnegie Mellon University uses permutation language modeling. The unique approach has enabled it to achieve state-of-the-art performance in various language tasks, including question answering and text generation.
  • BLOOM: BLOOM works much like GPT-3, but with one key difference – it is a multilingual LLM. The model was developed through collaboration efforts between different organizations and researchers.

Read more about What LLMs can you choose from?

Key factors to consider before implementing LLMs

Despite their numerous capabilities, LLMs can’t do everything. For successful implementation to benefit your organization, you need a model specially designed to fulfill all intended applications. You also need a model that’s able to adapt and scale to meet future organizational demands.

With that in mind, here are some of the key factors to consider before implementing your LLM:

Key factors to consider before implementing LLMs


Licensing is one of the greatest hindrances to LLM implementation, especially for commercial use. While most models allow you to fine-tune them for specific purposes that would benefit your business commercially, they also have active restrictions on commercial use. [7]

With that said, not all models fit within this criterion. There are a few models out there available for commercial applications. For instance, LLM models in the MPT Family and most models from MosaicML are released under licenses that allow commercial use.

Since it’s not always possible to know upfront, you need to rigorously read through every potential LLM license agreement. Besides giving you valuable information about any legal pitfalls the model may present, reading through the licensing agreement also enables you to ensure that the model perfectly aligns with your business model.

Task-specific vs. general purpose

What’s your intended purpose? Both task-specific and general-purpose LLMs have unique benefits and limitations, making them only suitable for specific applications and business objectives.

For instance, general-purpose LLMs can carry out a large variety of language-processing tasks using prompts. Their added versatility makes them suitable for a large variety of business applications, including chatbots and document-processing applications like text summarization and analysis.

On the other hand, task-specific LLMs are especially fine-tuned for specific tasks. Therefore, before you can implement them into your business structure, you first need to train them on task-specific data to enhance their performance and accuracy.

The latter, task-specific LLMs, provide a focused approach to problem-solving and enable businesses to optimize operations, thus providing better results. This makes them more ideally suited for practical business applications.

Inference speed and precision

Inference speed can be described as the time it takes for an LLM to process information and generate output, while precision is the model’s accuracy in sentiment analysis. Both factors play a crucial role in determining the suitability of an LLM model for practical applications.

Considering the inference speed and precision of an LLM model during your selection process is especially vital when dealing with vast amounts of data. Slow inference can inadvertently hinder information extraction, making it especially important to choose faster models.

The same notion applies to precision. For optimum effectiveness and efficiency, you need a model that’s precise and can perform fine-grained analysis seamlessly.

Evaluation and testing

Doing comprehensive research on the suitability of an LLM to your business operations is only half the journey – you also need to test and evaluate it rigorously to ascertain its suitability for specific purposes. [8]

One of the most effective methods of testing an LLM model is by creating a diverse test data set with diverse manually labeled examples. By using this form of annotation, you’re better able to get accurate measurements.

Once you test your LLM model, you can perform iterative improvement based on feedback evaluation and refine the model’s performance to provide desired outcomes aligned with service needs.

It might be also interesting for you: Unleashing the Large Language Models in Document Analysis

LLM implementation strategy

Review use cases from similar industries for inspiration

What’s your competition’s LLM implementation strategy? And how are they applying the models in their operations?

LLMs offer unparalleled, diverse capabilities for growing growth and efficiency in business. Every organization employs them uniquely. Therefore, by exploring potential use cases, you can take better steps at revolutionizing operations in your business and ultimately gain a competitive advantage.

Discover potential use cases in your organization

The key to a successful LLM implementation strategy lies in figuring out how to leverage it effectively. Therefore, the next step in your LLM strategy is identifying your organization’s specific business requirements that you can solve with LLMs.

While you’re at it, you also need to engage stakeholders, as they have a direct interest in the outcome of the LLM implementation project. Engaging stakeholders and different departments within the organization also ensures effective adaptation and long-term success of LLM implementation.

Prepare the technical background

A successful LLM implementation doesn’t just involve choosing the right model. You also have to account for the data needed to fuel the model and a proper budget to facilitate model training, deployment, and maintenance.

Your LLM implementation strategy should also have the effective groundwork for integrating the model into existing systems, including all current tools and workflows used in the business.

Once you have your LLM strategy in place, you need to monitor and evaluate the model’s performance. You can do this by continuously tracking the model’s performance and making necessary adjustments to improve output results and avoid unintended consequences.

Executing a successful LLM implementation

Regardless of the model you’re working with, executing a successful LLM implementation strategy can be a daunting undertaking. But, with the right strategies in place, you can effectively streamline your LLM implementation in a way that benefits your business.

Follow the following steps for a successful LLM strategy:

steps for a successful LLM implementation strategy

Set clear goals

You need an established aim for your LLM strategy to work. Setting clear goals right from the start provides applicability, focus, and uniformity in your interactions with the LLM. With a clear goal, you are better able to create customized prompts, questions, and limits to get the preferred output.

Customization also boosts the quality and applicability of generated content and simplifies the process of evaluating and assessing the quality of output.

Understand the model’s limitations

To use the model more effectively, you first need to understand its limitations. Doing this at the earlier stages of your LLM strategy helps set realistic expectations, interpolate material in context, find errors, apply ethical judgment, and enhance prompts.

Understanding the model’s limitations can also help you reduce its handicaps and increase generated content’s overall quality and dependability. This way, you are better able to advance and improve the model step by step.

Apply a hybrid strategy

A hybrid model essentially combines the capabilities of LLMs with human management and involvement. When it comes to implementing a successful LLM strategy, time and resource efficiency should be your biggest concerns.

While LLMs can generally perform operations quicker and more efficiently, bringing humans into the loop provides contextual understanding and quality control and facilitates the fine-tuning of answers. Additionally, humans are more accustomed to handling ethical considerations and incorporating creativity and originality into the output.

Monitor the model’s quality standards

Despite being trained on a corpus amount of data, LLM outputs aren’t always accurate or comprehensive. Therefore, in your LLM strategy, you need to continuously evaluate the model’s metrics to ensure that all generated outputs are accurate and of high quality.

Supply context and directions

Supplying context and directions ensures that the model produces dependable, precise, and applicable outputs that align with the organization’s goals. This is also another indication of why you need to have clear goals at the start of your LLM strategy.

By expressing your desired details, objectives, and restrictions, you can effectively ‘guide’ the model towards providing more suitable content and limiting bias. It also makes it easier to satisfy any quality metrics you’re tracking.

Gradually refine the output

Refining your output throughout your LLM strategy helps improve quality and applicability, optimize and customize the content, detect and fix mistakes, and provide better quality assurance and control. Refining your output also re-affirms the attitude of continuous learning and progress, which allows you to adjust user requirements and preferences as needed.

Verify and cross-check

LLMs are trained on massive amounts of data. While most of this data may be factual, there’s always the possibility of incorporating misleading information in the training data. Like any other machine, LLMs do what they’re trained to do and, thus, have no concept of truth or falsehood – they can only mimic human behavior using the information available to them.

That’s why it’s always important to verify every piece of information generated by a Large Language Model for accuracy, reliability, and trustworthiness. Cross-checking the mode’s output also helps identify errors and gaps, address bias, observe legal and ethical considerations, and encourage responsible content generation.

LLM Strategy Checklist

  1. Understand LLMs and their architecture.
  2. Choose between task-specific or general-purpose models.
  3. Consider licensing requirements.
  4. Evaluate inference speed vs. precision.
  5. Set clear implementation goals.
  6. Review potential use cases.
  7. Prepe aware of the model’s limitations.
  8. Apply a hybrid strategy for best results.
  9. Monitor quality standards closely.
  10. Provide sufficient context and direction.
  11. Refine outputs for accuracy.
  12. Conduct thorough evaluation and testing.
  13. Implement verification and cross-checks.

If your company is looking for expert assistance in building a LLM implementation strategy,
don’t hesitate to reach out to
Generative AI development company.

Wrapping up

Implementing a successful LLM strategy is an intricate process with a lot of moving parts. To ensure success, you first need to state your goals, identify a suitable model to meet them, train the model, and fine-tune it to meet the quality and accuracy demands of the intended purpose.

However, the journey doesn’t end with implementing the LLM into your organization. You also need to continuously monitor and improve the model to ensure reliability.


[1] Introduction to Computer Components. URL: Accessed August 21, 2023
[2] Transformer Model Architecture. URL: Accessed August 21, 2023
[3] Global Large Language Model Market Witnessed Rapid growth. URL: Accessed August 21, 2023
[4] What are Embeddings. URL: Accessed August 21, 2023
[5] Introduction to Positional Transformer Models. URL: Accessed August 21, 2023
[6] Understanding Encoder and Decoder LLMs. URL: Accessed August 21, 2023
[7] All About License for LLM Models. URL: Accessed August 22, 2023
[8] Large Language Model Evaluation. URL: Accessed August 22, 2023


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