One word describes the atmosphere whenever an organization embarks on a new AI app development project: excitement.
Everyone involved is excited about the possibilities. While development teams are dreaming about sleek conversational assistants, management is salivating over the possibility of predictive analytics on their dashboards.
Even the efficiency experts are envisioning automated workflows that save thousands of hours of labor.
The truth is that it is easy to get swept away by AI’s potential. But every software developer who has ever worked with large language models (LLMs) or deep learning tools is well versed in the hard, cold reality of AI: an AI tool is only as smart as the data it is fed.
That is why at GojiLabs, AI app development always begins with looking under the hood. Their teams do a deep dive into the data layer infrastructure long before the first line of code is read.
They do so because they know that launching an AI initiative without a comprehensive data audit is like building a home on top of an unknown sinkhole.
Fragmented, messy, and silo-trapped data all lead to an AI tool prone to mistakes.
Garbage Data Produces Hallucinations
Have you heard the phrase ‘garbage in, garbage out’? It usually describes the concept that what you feed your brain determines your mental and physical output.
If the input is bad, the output will be as well. The same principle exists in the world of AI app development.
Bad data won’t necessarily break traditional software. It will not always cause a report to look skewed. But in artificial intelligence, it is a different story.
Unstructured or unverified data leads to a model inventing facts or creating logical links that don’t really exist. We call this ‘hallucinating’.
GojiLabs explains that most enterprise data isn’t ready for AI applications out-of-the-box. It needs to be audited and structured to minimize potential hallucinations.
So first, an effective audit grabs data from every source: historical emails, PDF files, CRM entries, cloud folders, etc. Data is collected and cataloged. Gaps are identified and filled before any code is written.
All the raw information is then structured for AI optimization. Teams rely on tools like vector databases and semantic indexing to do the job. Properly structured data can be quickly searched, retrieved, and interpreted by an LLM to inform an accurate response.
Prototyping Is a Good Idea
GojiLabs also says that prototyping is smart as a project moves from the initial idea to proof of concept.
AI prototyping services can provide a lightweight proof-of-concept model before an organization dives into a massively expensive development cycle.
Prototyping takes a small portion of the newly structured data and runs it through AI models to see how it performs.
To ensure data layers are ready to support full development, the engineering team should always:
- Define the Scope – Pinpoint the exact problems the AI tool is designed to solve so that only relevant data streams are implemented.
- Create Cleansing Pipelines – Automated workflows capable of stripping out irrelevant information, handling missing values, and addressing duplicate data ensure a clean and reliable data stream.
- Enforce Security – Any sensitive information, like proprietary secrets and PII, must be strictly partitioned so the tool doesn’t accidentally leak data to unauthorized users.
It should be clear that preparing for AI app development is less about algorithms and more about data strategy.
A reliable digital product agency will always emphasize a robust, well-audited data layer over LLMs and algorithms.
Data is the foundation upon which AI rests. Auditing it is the key to developing a useful and reliable tool.