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Explore your unstructured data and power buisness and AI intelligence.
NLU Design
Train, evaluate and continuously optimize custom NLU models using unstructured data.
NLG Design
Guarantee prompt performance and observe LLM input/output at scale (beta).
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Google Cloud and Human First make it design, test , and launch scalable AI prompts and models you can trust using your unstructured data.
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Data Insights
Explore your unstructured data using NLU and prompts
NLU Design
Train, evaluate and continuously improve custom NLU
models using unstructured data
NLG Design
Guarantee prompt performance, and observe
LLM input/output data at scale (beta)
LLM fine-tuning (coming soon)
Prepare labeled data to fine-tune LLMs
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Add the intersection of NLU, LLMs and natural language data

RAG Evaluation

Retrieval Augmented Generation (RAG) is a very popular framework or class of LLM Application. The basic principle of RAG is to leverage external data sources to give LLMs contextual reference. In the recent past, I wrote much on different RAG approaches and pipelines. But how can we evaluate, measure and quantify the performance of a RAG pipeline?

COBUS GREYLING
5 min read

Follow-Up Interview With Cobus

The following is a redacted interview between Greg (CEO @ HumanFirst) and Cobus Greyling (www.cobusgreyling.me); in this interview, we dig deep into the intersection of NLU and voice / ASR technologies.

GREGORY WHITESIDE
9 MIN READ

Practical semantic search and clustering

Semantic similarity is quickly becoming a mainstream technique for working with natural language data; embedding new examples into a latent space uncovers the relationship between them. PineCone.io (among others) are doing a great job of producing content that describes how semantic similarity works under the hood.

GREGORY WHITESIDE
6 MIN READ

Interview with Cobus Greyling

[Greg] HumanFirst is a big fan of yours Cobus :) I've personally been reading your blog for the last two years, and have learned a lot about everything happening in Conversational AI, NLU and Call Center AI thanks to your deep-dives on existing solutions and prolific articles covering a wide range of topics.

GREGORY WHITESIDE
7 MIN READ

Are we focusing too much on the labeling problem?

Programatic labeling has been a hot area of development in the last 2 years, with Snorkel Flow leading the way, and other commercial and open-source projects appearing in this space.

GREGORY WHITESIDE
6 MIN READ

Voice of the Customer Data is 🚀

Most businesses are sitting on high-quality Voice of the Customer Data , generated across a growing number of channels in the context of sales, marketing, product and operations.

GREGORY WHITESIDE
4 MIN READ

GPT-3 in Conversational AI

The first iteration of HumanFirst was a browser extension that ingested customer support live chat conversations, trained a black-box AI model that predicted what the chat agent’s next response should be, and provided auto-complete suggestions to the agents to speed up their replies.

GREGORY WHITESIDE
5 MIN READ

Help Centers are Underrated in Conversational AI.

> In this article, I use the term "Help Center" to refer to all types of Help Center, Knowledge-base and FAQ solutions. Help Centers are rarely a core part of the

GREGORY WHITESIDE
4 MIN READ

Labeling isn't hard if you know what to label.

Companies like Scale.ai [scale.com] (recently valued at $7B+ dollars [https://fortune.comscale-ai-valuation-new-funding-fundraising-data-labeling-company-startups-

GREGORY WHITESIDE
2 MIN READ

Conversational AI should start with search.

Let’s take an airline example: “I want to change my flight from the 3rd to the 4th”.

GREGORY WHITESIDE
3 MIN READ

Walking before running in conversational AI.

Conversational AI is often equated with chatbots. Yet chatbots rarely do the “AI” part correctly (i.e: understanding user intents).

GREGORY WHITESIDE
2 MIN READ
Articles

Building a Semantic Emoji Prediction NLU

Learn one way to approach semantic Emoji prediction with cascading intents.

GREGORY WHITESIDE
6 MIN READ
Articles

The crux of NLU is managing the long-tail of data.

> Until now, building and maintaining the training data needed to support a long-tail of intents has been a difficult problem — yet this is what is needed to go from a Gregory Whiteside.

GREGORY WHITESIDE
2 MIN READ
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