Large Language Models (LLMs) operate probabilistically – they approximate facts rather than reproduce them exactly.
Training data is never fully complete or consistently accurate, and its truthfulness is difficult to measure. The result: an LLM produces outputs that may appear plausible but do not always deliver what we actually expect.
Why Evaluation Is Essential
LLMs are said to “hallucinate” – a commonly used term, but one that only partially captures the challenge. We prefer to speak of “perplexity”:
- The model may overlook information
- Misinterpret relationships
- Or simply generate answers that are not reliable
Unlike traditional IT systems or databases, LLMs lack transparent traceability—there are no database entries or logs that clearly explain why a particular result was produced.
This makes simple testing and evaluation far more complex.
Guardrails and Hybrid Approaches
To make LLMs practically usable, we apply Guardrailing or Truthgrounding. The core principles of these approaches are:
- Extending and enriching the context deliberately
- Guiding the LLM within well-defined boundaries
- Building hybrid systems that combine generic AI knowledge with domain-specific context
This is how integrated AI systems are created—more reliable and business-relevant.
For complex enterprise applications—such as:
- Insurance claims assessment
- Mortgage loan applications
- Automotive lease returns
- Travel expense approvals
— we consistently rely on hybrid architectures.
Pure “generic LLMs” without contextual constraints are currently not reliable enough for these use cases.
Our Approach at sol4data
We support companies from use-case selection through evaluation, including:
- Selecting the right model and mapping it to the business case
- Designing hybrid, integrated AI architectures
- Building pipelines, guardrails, and evaluation processes
For us, evaluation means benchmarking and testing—measuring accuracy and perplexity against defined expectations.
The particularly challenging aspect: LLMs have a much larger solution space than traditional IT systems, making testing more extensive and complex.
Our Goal
AI systems that don’t just impress, but reliably do what they are supposed to do—and generate real business value.
Book a session with one of our AI architects for a guided discussion on evaluating AI systems!
