The AI Brief: Smart prompting and African innovation

Thinking about communication professionals who need practical tools that work

We are working on improving our prompting game. Corporate comms teams, newsrooms and creatives are going to need to know how LLMs work and how to optimize prompting. We have decided that we need to know much more than the basics which are typically:

• give the LLM a role

• a goal

• examples

• context

• tone

• output format

We are digging into how we can create prompts in relevant situations that we would use every day. We are discussing our repeated tasks and developing prompts for those that are also consistent with the values and the ethics of our company. The ones that are awesome and usable we will save for future reference in our database. We are also using Markdown to assist our prompt output requirements. We will share learnings with you.

I encourage everyone to be upping this game of prompt refinement. Here’s an example: Prompt Station from Superhuman Newsletter. Simplify and summarize long articles

Prompt: You are a reading assistant. The user will provide you with an article to read. Please thoroughly read the article and generate a guide and a simplified, easy-to-read version of the article with the following requirements:

## Your output:

Mind Map: First, generate a mind map for the entire guide.

Summary: Next, provide a summary of the entire text, limited to 400 words.

Simplified Article: Then, present your rewritten simplified and easy-to-read version of the article.

## Requirements for Rewriting:

Word Count: Compress the word count to half of the original, and ensure it does not exceed 3,000 words.

Author’s Tone: Simulate the original author's tone.

Structure: Maintain the original structure, retaining all levels of headings.

Formatting: Format the output to ensure a visually appealing layout and ease of reading.

Visual Elements: Insert tables, charts, diagrams, SVGs, and other visual elements in appropriate places to enhance readability.

Source: r/ChatGPTPromptGenius
How Constraint-Driven Innovation Is Producing Better Solutions Than Billion-Dollar Budgets

Snake Mode

I’m taking baby steps on thinking in code and understanding Python. Gulp. I’m not trying to be a developer, but I’m trying to improve my knowledge that will enable me to understand what I am looking at or need to be thinking about when building with AI agents and workflows. Thanks to my Co.Founder Thomas for the steer ! I am actually LOVING IT !

I also built myself a daily course nugget on Python baby steps on Gemini Learning Coach. And one on Bitcoin called “What would Saylor Say.” … There is no second best …

One Drama

From Superhuman:

OpenAI has been trying for months to transform into a public-benefit corporation, giving it a chance to turn a profit and accelerate its growth. The one thing holding it back: Microsoft won’t budge on the details out of fear it’ll get burned in the process. According to the Wall Street Journal, the feud has gotten so heated that OpenAI is now considering going public with its accusations. It could also ask the federal government to investigate Microsoft for antitrust violations.

Embedded Podcast Promo

Please do subscribe to my newsletter. It’s $8 a month. I know you’re trying to reduce bwana. Ama , Saidia ! We are planning to increase the knowledge sharing on our Substack and focus more deeply on communiciations teams, newsrooms and creatives offering tips and ideas on … everything useful!

Let me know if you want more me in your inbox or it’s enough.

Africa Builds AI That Serves Africans

According to Developing Telecoms and Tech African News, Cassava Technologies just announced a partnership with Vambo AI to develop large language models specifically trained for African languages and contexts. This goes far beyond translation, the goal here is building AI systems that understand cultural nuances, local economic realities, and the specific challenges of operating across diverse African markets.

Most AI models are trained primarily on English and European languages, with limited understanding of African contexts. When you try to use ChatGPT to analyze a business problem in Swahili, understand South African economics, or navigate Nigerian regulatory requirements, you're working with a system that has fundamental knowledge gaps.

Cassava's "AI factory" is equipped with Nvidia GPU-based supercomputers—the same technology that powers OpenAI and Google's systems. But instead of trying to compete with Silicon Valley on scale, they're focusing on relevance and cultural competence.

The business implications are significant. If you're a multinational company trying to operate across African markets, AI systems that understand local languages, cultural contexts, and business practices could provide massive competitive advantages. Imagine customer service systems that understand not just the words customers use but the cultural context behind their requests.

Constraint Drives Innovation: The 75% Model Compression Story

Teams from the continent have just figured out how to shrink an African language AI model so it works on entry level phones that are most used in emerging markets.

490 people from 61 countries competed to compress InkubaLM, Africa's first multilingual Small Language Model, without losing performance. The winners—all from Africa managed to shrink the model by 75% while maintaining its capabilities.

In Silicon Valley, bigger is usually considered better. Larger models with more parameters can handle more complex tasks. But this approach assumes unlimited computing resources and constant internet connectivity. In Africa, 70% of people rely on entry-level smartphones with limited processing power and patchy internet connectivity. A massive AI model that requires constant cloud connection is useless if your target users can't access it reliably.

• Yvan Carré from Cameroon took first place using techniques like adapter heads, quantization, and knowledge distillation.

• Stefan Strydom from South Africa cut the model to just 40 million parameters through vocabulary trimming and shared embeddings.

• The AI_Buzz team from Niger and Nigeria used blended datasets and model distillation.

  

PS. I hope to interview them on the Embedded Podcast Series. Do listen to the work we have been doing to highlight African AI innovators and leaders.

Continuing…

These compressed models can run locally on basic devices, enabling AI applications in education, agriculture, translation, and customer service across the continent without requiring expensive infrastructure or reliable internet connections.

This approach offers a different model for AI deployment in Africa. Instead of assuming users have the latest devices and perfect connectivity, you design systems that work within real-world constraints. This often results in solutions that are actually more robust and accessible.

Takeaway:African teams are solving real problems with elegant solutions. The lesson applies everywhere: the most practical AI innovations often come from understanding your constraints, not ignoring them.

Kids Becoming AI Dependent

From

InvestAnswers

A UK study from The Alan Turing Institute highlights the growing use of generative AI among children aged 8-12, with 22% already using it. However, a significant digital divide exists: private school students are three times more likely to have access than state school students (52% vs. 18%). This gap is worsened by private schools reporting more frequent AI use and greater teacher awareness. The Challenges:

Children may become dependent on AI for homework and problem-solving, potentially hindering independent learning and creativity - especially first principles thinking and problem solving... the two most imp skills. Interacting with AI instead of humans can reduce opportunities for developing social skills and emotional intelligence, potentially leading to isolation.

Asante. Thanks.

Zain