To move forward we often need to look back to retrace our steps, measure success and learn from our mistakes. Similar to the checks and balances we apply to the software development lifecycle. At a time when the quality of data is so fundamental to application performance, it makes sense to apply those same practices to the data lifecycle. Precise tracking and monitoring ensure the quality, efficiency and reliability of data pipelines. But when it comes to evaluating the data hungry LLMs that underscore Gen AI applications, data lineage can track the origins and movements of data throughout its lifecycle.
On the surface, LLMs create outputs that sound authentic and polished, but they are also inherently non-deterministic and prone to hallucinations—the generation of inaccurate, irrelevant, or potentially harmful responses. Also, LLMs are at risk of being hacked and being fed false data, which can distort their responses. As a result, LLMs must be rigorously evaluated for quality and safety, particularly due to their non-deterministic nature. In addition, organisations should seriously consider putting guard rails in place to prevent LLMs from absorbing and relaying illegal or dangerous information.
Assessing LLM data
Working with LLMs is a fundamentally new type of skill that traverses software engineering, product management and data science. It requires learning new patterns and frameworks. Enhanced observability and monitoring along with a focus on data lineage can help identify when LLMs have been compromised. These techniques are crucial in strengthening the integrity and security of an organisation’s Gen AI products.
LLM observability plays a critical role in enhancing the security of LLM applications. By tracking access patterns, input data, and model outputs, it’s possible to detect anomalies caused by data leaks or malicious attacks. This allows data scientists and security teams to proactively identify and mitigate security threats, protect sensitive data, and ensure the integrity of LLM applications. While it’s necessary to implement measures to prevent LLMs from being breached, it is equally important to closely monitor data sources to ensure they remain uncorrupted.
By questioning the security and authenticity of the data, as well as the validity of the data libraries and dependencies that support the LLM, teams can critically assess the LLM data and accurately determine its source. Consequently, data lineage processes and investigations will enable teams to validate all new LLM data before integrating it into their Gen AI products. This approach will help to ensure that LLM applications operate reliably and ethically, addressing both performance and safety concerns.
Securing Gen AI products
Ensuring the security of AI products is essential, but organisations must also maintain the ongoing quality of performance. Software engineering and DevOps teams can employ new AI debugging techniques like clustering, which allows them to group events to identify trends, to ensure the optimal performance for those products.
The ability to fix AI-related bugs, whether in the lab or in the wild, will become a necessity. This ability will help enhance the performance of generative AI agents and other products and services.
Adopting a more streamlined and centralised approach to collecting and analysing data clusters will save time and resources, enabling engineers to drill down to the root of issues – such as inaccurate responses – and address them promptly.
Controlling the data lifecycle
The implementation of new Gen AI products significantly increases the volume of data flowing through businesses today. As a result, organisations must be cautious about the data they provide to the underlying LLMs and how that data is translated and presented to end users. Organisations must be aware of the type of data they provide to the LLMs that power their AI products and, importantly, how this data will be interpreted and communicated back to customers. They can achieve this by controlling the data their LLMs have access to, or by establishing checks and balances to monitor what data they’re fed. Additionally, organisations should recognise that LLMs can be susceptible to hacking and manipulation through false data inputs, which can skew their responses.
LLM-based applications are incredibly powerful and unlock many new product opportunities, but there is a pressing need for granular visibility into their performance and behaviour. Preventative measures must be taken to protect LLMs from breaches, and close attention should be paid to the integrity of data sources to ensure they haven’t been compromised. In this regard, LLM observability will play a vital role in monitoring data pipelines to ensure that the information provided to end users is accurate, safe and reliable.
