Semantic Analysis vs. LLMs: Why Meaning Is a Better Motivator
- Steffen Konrath
- 3 days ago
- 5 min read
Is it enough to use generative AI for analysis – or do we need an approach that understands meaning rather than probabilities?
While LLMs like GPT reproduce information, evAI uses Semantic Analysis to generate new knowledge that defines brand spaces and serves as an early warning system for markets and narratives.
This article shows why semantics delivers more than statistics – and how brands, executives, and companies can make informed, future-relevant decisions.
How does semantic analysis fundamentally differ from LLMs?
TL;DR: LLMs build on the past based on probabilities – evAI recognizes how the future is thought of, even if the evidence for it is so rare that no probability model can capture it.
Evidence
LLMs calculate probabilities from Big Data; evAI builds reality models from meaning structures.
evAI does not analyze the frequency of words, but rather thought patterns, narratives, and relationship spaces.
Results are structured models (actors, risks, influence networks), not text summaries based on probabilities.
LLMs provide secondary data based on statistical correlations; evAI generates primary data based on factual and logical connections, making it usable for strategy, M&A, communications, and policy.
Why LLMs hallucinate – and why evAI doesn’t
TL;DR: LLMs are language prediction engines, not knowledge systems. They generate plausible, but often false statements, because they value coherence over truth. evAI, on the other hand, utilizes meaning and context, not probability.
Evidence
According to the study “ Why Language Models Hallucinate ” (Kalai et al., 2025), hallucinations occur because LLMs are rewarded for “ guessing instead of not knowing ”.
Their training goal, to generate the most probable next word sequence, prefers fluent formulations over factual accuracy.
The models do not “ know ” when they lack knowledge: they simulate understanding rather than reconstructing meaning.
Hallucinations are not a bug, but a statistical byproduct of probability optimization.
Even with error-free training data, false statements arise – as a natural consequence of the cross-entropy goal (=optimizing the probability of correctly predicting the next word, whether it is factually correct or not. The only thing that matters is higher probability). Put simply: An LLM generates the most probable answer.
evAI, on the other hand, works semantically validated: information is checked according to context and meaning, not evaluated according to probability.
Why is Small Data more meaningful than big data?
TL;DR: LLMs close answer gaps at the micro level by hallucinating. They invent answers if necessary. evAI uses Small Data to identify topics that are so new that there is little or no historical data. In our view, the relevance and quality of the answer (evAI's Small Data approach) outperform statistical response behavior (LLMs) – a few precise signals can be decisive. These Small Data events are " invisible " to LLMs, which rely exclusively on the most likely answer corresponding to the largest sample size. Quantity, however, does not equal relevancy.
Evidence
According to evAI research, 80–90% of the analysis time of traditional monitoring tools is spent on irrelevant sources that only increase the noise, losing focus.
Relevant topics diffuse multiple times – you don’t have to count them millions of times, but rather recognize them semantically.
Weak signals emerge at the margins of discourse, where Big Data has no trace and where they cannot be perceived by LLMs.
Small Data enables early warning of disruptions where little or no experience has been gained in the past.
How does evAI's Semantic Framework work technically?
TL;DR: evAI combines its own ontology, context analysis, and meaning spaces (similar to namespaces) into a dynamic reality model.
Components
Ontology-based semantics: Terms are linked according to meaning and context, not frequency.
Semantic mapping: creates “ maps of thought ” with actors, narratives, interests, and conflicts.
Semantic sensors measure changes in discourses – e.g., when “normality is shaken.”
The results are contextual models that provide strategic orientation, instead of purely probability-based snapshots.
Which 6 criteria determine whether an AI has early warning quality?
TL;DR: Only systems that measure importance, diffusion, and relevance can identify risks and opportunities early.
Criteria (ranking)
Relevance filtering – Does the system only capture thematically relevant sources?
Semantic depth – Does it understand meanings/content rather than keywords?
Diffusion analysis – Does it recognize how topics spread?
Noise reduction – Does it separate the signal from the noise?
Leading indicators – Does it identify early issues before they become mainstream?
Adaptivity – Does it adapt to new discourses?
evAI fulfills all six – classic social listening or LLM systems usually only one or two.
LLMs or Semantic Analysis – Which is better for market intelligence?
TL;DR: LLMs are research tools; semantic analysis is a strategic navigation system.
Comparison
criterion | LLM | evAI Semantic Analysis |
Database | Big Data (scraped) | Small Data (curated primary data) |
Type of knowledge | Probabilities | Meaning structures |
Result | Text summary | Market model with actors, narratives, risks |
Time horizon | Looking back | Today + Looking Ahead |
Explainability | Small amount | High (ontologically comprehensible) |
use | Secondary source | Primary source for strategy & LLMs |
Methods & Data Appendix
Database: Curated, domain-specific information objects (policy papers, specialist media, interviews, Tier 1 to Tier 3 sources, and, if necessary, special channels such as Telegram, Dark Net, or social media).
Analysis method:
Ontology-based context analysis
Semantic diffusion measurement
Weak signal detection
Modeling of reality perceptions
How does a 5-day rollout with evAI work?
TL;DR: From question to action recommendation in five days – exploratory, model-based, without data overload.
Process
Day 1: Define research question & scope.
Day 2: Building a semantic model with actors and narratives.
Day 3: Analyze small data, identify weak signals.
Day 4: Validation & hypothesis testing – making the unexpected visible.
Day 5: Deliver action plan & monitoring options with the result: actionable insights that flow directly into board meetings
How do companies use semantic models in practice?
TL;DR: From market intelligence to crisis prevention – semantic models provide strategic early warning signals.
Use Cases
Energy & Water: Detecting bottleneck signals months before measurable effects.
M&A / Strategy: Identification of actor networks and lobby structures.
Retail / Sustainability: Separation between hype and real adaptation.
Communication: Narrative analyses and position gaps in the market model.
Governance: Early detection of discourse shifts and risk cluster formation.
FAQ
What is the main difference from NLP? → NLP counts words; evAI understands meanings.
Does evAI need a lot of data? → No. Often, less than 200 signals are enough to detect disruptions.
How quickly are results available? → Within five days – instead of weeks as in traditional research.
Can I combine LLMs and evAI? → Yes. evAI provides primary data that LLMs can use to provide more in-depth answers than an expert system.
Is the method explainable and GDPR-compliant? → Fully traceable and using only legal, open sources.
Learn how Semantic Analysis transforms your monitoring into an early warning system.
