Over the past few years, Generative AI has gone from being a buzzword to a backbone. It’s transforming how we create, analyze, and understand conversations. At the heart of this revolution are Large Language Models (LLMs), i.e. the engines behind tools like GPT, Claude, and many others. What began with the Transformer architecture in 2017 sparked a chain reaction: models got smarter, faster, and more nuanced.
Today, these capabilities are reshaping every aspect of work, including how we at TFT help brands extract richer consumer insights, decode emotional nuance, and take sharper marketing action.
What Transformed Language Models
Consider these two sentences:
1. The deer did not cross the road because it was full of speeding cars.
2. The deer did not cross the road because it got scared.
As humans, we effortlessly resolve the pronoun “it”: in the first case, it refers to the road; in the second, the deer. This kind of interpretation depends on context, causality, and commonsense knowledge.
For machines, this was once near impossible.
From Static to Dynamic Understanding
Earlier models like Word2Vec or GloVe used static word embeddings; here, every instance of “bank” or “it” had the same representation, regardless of context. These tools couldn’t distinguish between a riverbank and a financial bank, or between a scared deer and a busy road.
The breakthrough came with attention mechanisms. These let models weigh the surrounding context dynamically: “it” in Sentence 1 now attends to “road,” “cars,” “speeding”; in Sentence 2, it attends to “deer” and “scared.” Meaning is no longer fixed, it’s computed on the fly.
This led to the Transformer architecture. Introduced in 2017, it was a model that ditched recurrence (RNNs) for parallel processing of all the inputs using the attention mechanism. This shift unlocked massive performance improvements and critically, scalability.
Accessible Compute Power
But architecture alone wasn't enough. What truly accelerated the rise of LLMs was the exponential growth in compute power. Cloud GPUs, TPUs, and distributed training infrastructure made it possible to train models with billions — and now trillions — of parameters. What once took weeks or months could now be trained in days.
With greater compute came the ability to feed these models with more data, iterate faster, and refine outputs across diverse tasks. The Transformer wasn’t just a technical innovation; it arrived at the perfect moment, riding a wave of hardware evolution that made large-scale language modeling practically feasible.
GPT: The Transformer at Scale
GPT models take the Transformer’s decoder and train it to predict the next word in vast sequences. With billions of parameters, they internalize patterns across languages, domains, and contexts, enabling zero-shot reasoning, summarization, emotional tone detection, and more.
We moved from machines that looked up definitions to machines that model relationships, imply emotion, and adapt to nuance.
What This Means for Consumer Insights and Research
The rise of LLMs is more than a tech evolution, it reshapes how insights are uncovered.
At TFT, we specialize in deep qualitative research powered by marketing science, neuroscience, and anthropology. With the emergence of LLMs, we’ve integrated them into our insight workflows via our AI platform PercepSense— transforming how we listen, analyze, and decode consumer conversations.
Here’s how these models change the game for researchers:
New Superpowers for Researchers and Marketers
Contextual nuance at scale: LLMs can track layered meaning across long transcripts; surfacing subtext, contradiction, and ambiguity.
Fine-grained emotional detection: From subtle cues in phrasing, LLMs can now pick up on deep emotional signals embedded in text, even when unspoken or ambiguous.
Advanced NLP for insight extraction: Tasks like topic detection, semantic tagging and classification can now be done with precision.
Realtime synthesis and theming: Moderation insights aren’t trapped in field notes. They’re surfaced and visualized in mere minutes.
From Conversations to Clarity
Through PercepSense, our AI conversation engine, we go beyond tagging words and summarization. We extract patterns of thought, shifts in emotion, and implicit brand narratives — all grounded in real human conversation. And because our tools are designed by researchers for researchers and marketers, they reflect our values: depth, curiosity and cultural intelligence.
We don’t just use LLMs. We train and fine-tune them with qualitative data structures in mind to bring the best of machine learning to the service of meaning-making.
Just Better Insights
Our goal at TFT isn’t to automate research, but to amplify it by leveraging our Gen AI tools like PercepSense to give you more signal, more precision, and more time to think.
If you're curious about what AI-powered insight generation actually feels like, let's talk. We're always open to demos, pilots, and collaborations.
