Data Engineering

Unlocking the Power of Large Language Models: Key Considerations for Successful Implementation

10 Oct 2023
Unlocking the Power of Large Language Models: Key Considerations for Successful Implementation



Getting the pipeline right is crucial


The large language model (LLM) revolution is letting many businesses automate their processes and effortlessly create text for all kinds of needs.


While it might look like the LLM itself is the most important part of the puzzle, just as Google have suggested that Machine Learning model code is less than 5% of a machine learning pipeline, the code to call the LLM is only a tiny piece of the LLM pipeline. Getting the rest of the pipeline right is crucial to crafting an effective LLM implementation – and there are multiple places where not getting it right can lead to an implementation that doesn’t deliver the results you’re looking for.



Discovery of the user’s goals

asking the right questions is vital


Any successful project starts with a clear understanding of the user’s goals, but any experienced data scientist knows that users usually aren’t able to clearly identify what they want on the first attempt. Accurately stating the user’s goal is the fundamental start of any tech project, and projects using LLM’s are no exception. Users may not know how to explain what they want the first time, so spending time with the end user and asking the right questions is vital.


Successful validation of an LLM

Propose an evaluation metric and success criteria


Working towards the goal of achieving a certain level of an evaluation criteria is a crucial step when training a machine learning model. In this context, many of the validation metrics are well known. Successful validation of an LLM requires you to develop and apply new validation metrics that align with the needs of your users, and support their goal.


experimentation with the outputs is essential

Control of experimentation and interpretation of results


The outputs of LLMs are inherently probabilistic, so experimentation with the outputs is essential. Understanding how to record, analyse and act on experimental results is, in turn, one of the fundamental skills of a statistician or data scientist — and what the statistics discipline was built on.  To design the your experiments properly, you’ll need a data scientist who has experience in A/B testing or basic experimental design, and can harness that expertise to improve the stability of your prompts.


understanding the requirements

Extracting Existing Text and Writing The Results

Many of the most exciting applications of LLMs use existing text – for example, summarising or proofreading documents.  Moreover, the results of LLMs themselves generally need to be formatted to be accessible to the end user.  Therefore a crucial part of getting an LLM pipeline right is ensuring that those requirements are understood, and ensuring that the final product is formatted the right way.



version control maintainS integrity


Using version control maintains integrity of fine tuning settings and optimal prompts, as well as for test inputs and outputs.


A crucial part of MLOps is monitoring the newly implemented model as it starts to be used in its application, and ensuring that any model drift is minimised. The same applies to a newly implemented LLM, where the prompt that gave you a great response yesterday may not give the same quality tomorrow. Monitoring responses and assessing when prompts need to be updated will ensure your implementation of an LLM continues to meet the goals you set with the users in the initial phases.





The success of a LLM pipeline can be derailed if any of these factors doesn’t work as expected. To ensure your next LLM project is successful use a team that has the expertise to ensure every part of the is successful.


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