Monday, August 7, 2023

How to run webinars

Why do you care about running webinars?

For sales and marketing people, the answer is obvious, to generate leads and do your job. For technical people, things are a little murkier, and as a result, technical people sometimes make avoidable mistakes when they give webinars.

In this blog post, I'll explain how and why a technical person should approach and run webinars. At the end, I'll link to a longer report where I go through the entire process from start to finish.

My experiences

I've run webinars in big companies and small companies and I've had my share of problems. I've faced visual and audio issues, planning issues, marketing issues and on and on. I've learned from what went wrong so I can advise you on what to do.  Here's my summary advice: make sure you understand the whole process end-to-end so you can step in to fix any shortcomings.

What value do you bring?

Why should anyone come to your webinar? 

The marketing department may have asked you to do a webinar, but frankly, they're not going to answer this question for you. If it isn't clear why anyone should attend your webinar, then you're not going to get a good audience. Webinars are not free to attend: they cost your attendees their time, which is extremely valuable. To justify spending someone's time, here are some questions you should ask:

  • who should attend?
  • what will they learn?
  • what will they take away?

Before you do anything, you need to be clear on these points.

Let's take an example of where engineers fall down: webinars for new minor releases. The marketing team wants a webinar on the new release with the goal of increasing leads. The problem is, the new release is a minor one, really only of interest to existing customers. Unfortunately, the engineering team will only commit resources to a release webinar, so that's what gets scheduled. This is a common siuation and the irreconcilable conflict of goals and resources and will lead to the webinar failing. In this case, the engineers and the marketing team need to discuss what's really needed, perhaps there are two webinars, one focused on the new functionality for existing customers and a new webinar on the product overall for prospects. It needs an honest discussion in the company.

I go into this in a lot more detail in my report.

Is the marketing in order?

In almost all cases, the goal of a webinar is to generate sales leads. Usual measures of success are leads generated or sales contributions. To be successful then, the marketing behind the webinar must be effective. This means:

  • a clear and unambiguous value proposition
  • a compelling summary
  • a clearly defined market demographic (e.g. the job titles and organizations you want to reach)
  • an effective recruitment campaign (registration page, social media outreach, email etc.)
  • a compelling call to action at the end of the webinar (e.g. register for more content)

If some or all of these steps are missing, the webinar will be a disappointment.

These steps are usually under the control of the marketing department, but I've done webinars where some or all of these steps were missing and the results were'n't good. Even if you're a completely technical person, you need to ensure that the marketing for your webinar is effective.

Does the webinar have a good story?

This means the webinar must tell a compelling story and have a consistent narrative with a begining, middle, and end. It should finish with a clear and unambiguous call to action.

A good test of whether you have a good story is the 30 second summary. Summarize your webinar in a 30 second pitch. Does it sound good? If not, try again.

Is the audio-visual setup good enough?

Some of this is obvious, but some of it isn't. Audio filtering can clean up some background noises, but not others, for example, you can't filter out echoes. Here's my checklist:

  • Good quality microphone plus a good pop filter - the pop filter is extremely important. 
  • Record your webinar in an acoustically quiet environment. This means few background noises and as much sound deadening material as possible. A bedroom is good place to record a webinar because all the soft furnishings help deaden noise.
  • Make sure your demos work end-to-end. If at all possible, pre-record them and play the recording out during the webinar (but be careful about the technology).

Duration and Q&A

Don't do more than 25 minutes and stick to your schedule. Don't overrun. Leave your audience wanting more, which means you can offer more material as a follow-up (and excuse for more interaction and selling).

Don't leave Q&A to chance. Have a set of canned questons and answers ready to go if your audience is slow to ask questions or the questions are ones you don't want to answer.

The complete guide

This is a small taster of what you have to do to make a webinar succesful. I've expanded a lot on my thoughts and written a comprehensive guide, covering everything from microphone selection to landing pages. You can get my guide by clicking on the link below.

Thursday, August 3, 2023

Using ChatGPT for real to interpret text

What's real and what isn't with ChatGPT?

There's a huge amount of hype surrounding ChatGPT and I've heard all kinds of "game changing" stories around it. But what's real and what's not?

In this blog post, I'm going to show you one of the real things ChatGPT can do: extract meaning from text. I'll show you how well it performs, discuss some of its shortcomings, and highlight important considerations for using it in business. I'm going to do it with real code and real data.

We're going to use ChatGPT to extract meaning from news articles, specifically, two articles on the Women's World Cup.

D J Shin, CC BY-SA 3.0, via Wikimedia Commons. I for one, welcome our new robot overlords...

The Women's World Cup

At the time of writing, the Women's World Cup is in full swing and England have just beaten China 6-1. There were plenty of news stories about it, so I took just two and tried to extract structured, factual data from the articles.

Here are the two articles:

Here is the data I wanted to pull out of the text:
  • The sport being played
  • The competition
  • The names of the teams
  • Who won
  • The score
  • The attendance
I wanted it in a structured format, in this case, JSON.

Obviously, you could read the articles and just extract the information, but the value of ChatGPT is doing this at scale, to scan thousands or millions of articles to search for key pieces of information. Up until now, this has been done by paying people in the developing world to read articles and extract data. ChatGPT offers the prospect of slashing the cost of this kind of work and making it widely available.

Let's see it in action.

Getting started

This example is all in Python and I'm assuming you have a good grasp of the language.

Download the OpenAI library:

pip install openai

Register for OpenAI and get an API key. At the time of writing, you get $5 in free credits and this tutorial won't consume much of that $5.

You'll need to set your API key in your code. To get going, we'll just paste it into our Python file:

import openai
openai.api_key = "YOUR_KEY"

You should note that OpenAI will rescind any keys they find on the public internet. My use of the key in code is very sloppy from a security point of view. Only do it to get started.

Some ChatGPT basics

We're going to focus on just one part of ChatGPT, the ChatCompletion API. Because there's some complexity here, I'm going to go through some of the background before diving into the code.

To set the certainty of its answers, ChatGPT has a concept of "temperature". This is a parameter that sets how "sure" the answer is; the lower the number the more sure the answer. A more certain answer comes at the price of creativity, so for some applications, you might want to choose a higher temperature (for example, you might want a higher temperature for a chatbot). The temperature range is 0 to 1, and we'll use 0 for this example because we want highly reliable analysis.

There are several ChatGPT models each with a different pricing structure. As you might expect, the larger and more recent models are more expensive, so for this tutorial, I'm going to use an older and cheaper model, "gpt-3.5-turbo", that works well enough to show what ChatGPT can do.

ChatGPT works on a model of "roles" and "messages". Roles are the actors in a chat; for a chatbot there will be a "user" role, which is the human entering text, an "assistant" role which is the chat response, and a "system" role controlling the assistant. Messages are the text from the user or the assistant or a "briefing" for the system. For a chatbot, we need multiple messages, but to extract meaning from text, we just need one. To analyze the World Cup articles, we only need the user role.

To get an answer, we need to pose a question or give ChatGPT an instruction on what to do. That's part of the "content" we set in the messages parameter. The content must contain the text we want to analyze and instructions on what we want returned. This is a bigger topic and I'm going to dive into it next.

Prompt engineering part 1

Setting the prompt correctly is the core of ChatGBP and it's a bit of an art, which is why it's been called prompt engineering. You have to very carefully write your prompt to get the results you expect.

Oddly, ChatGPT doesn't separate the text from the query; they're all bundled together in the same prompt. This means you have to clearly tell ChatGPT what you want to analyze and how you want it analyzed.

Let's start with a simple example, let's imagine you want to know how many times the letter "e" occurs in the text "The kind old elephant." Here's how you might write the prompt:

f"""In the following text, how often does the letter e occur:

"The kind old elephant"


This gives us the correct answer (3). We'll come back to this prompt later because it shows some of the pitfalls of working with ChatGPT. In general, we need to be crystal clear about the text we want the system to analyze.

Let's say we wanted the result in JSON, here's how we might write the prompt:


In the following text, how often does the letter e occur, write your answer as JSON:

"The kind old elephant"


Which gives us {"e": 3}

We can ask more complex questions about some text, but we need to very carefully layout the query and distinguish between text and questions. Here's an example.

prompt = f"""

In the text indicated by three back ticks answer the \

following questions and output your answer as JSON \

using the key names indicated by the word "key_name" \

1) how often does the letter e occur key_name = "letter" \

2) what animal is referred to key_name = "animal" \

```The kind old elephant```


Using ChatGPT

Let's put what we've learned together and build a ChatGPT query to ask questions about the Women's World Cup. Here's the code using the BBC article.

world = """

Lauren James produced a sensational individual 

performance as England entertained to sweep aside 

China and book their place in the last 16 of the 

Women's World Cup as group winners.

It was a display worthy of their status as European 

champions and James once again lit the stage alight 

in Adelaide with two sensational goals and three assists.

The 13,497 in attendance were treated to a masterclass 

from Chelsea's James, who announced her arrival at the 

World Cup with the match-winner against Denmark on Friday.

She helped England get off to the perfect start when 

she teed up Alessia Russo for the opener, and 

later slipped the ball through to Lauren Hemp to 

coolly place it into the bottom corner.

It was largely one-way traffic as England dominated 

and overwhelmed, James striking it first time into 

the corner from the edge of the box to make it 3-0 

before another stunning finish was ruled out by video 

assistant referee (VAR) for offside in the build-up.

China knew they were heading out of the tournament 

unless they responded, so they came out with more 

aggression in the second half, unnerving England 

slightly when Shuang Wang scored from the penalty 

spot after VAR picked up a handball by defender 

Lucy Bronze.

But James was not done yet - she volleyed Jess Carter's 

deep cross past helpless goalkeeper Yu Zhu for 

England's fourth before substitute Chloe Kelly and 

striker Rachel Daly joined the party.

England, who had quietly gone about their business 

in the group stages, will have raised eyebrows with 

this performance before their last-16 match against 

Nigeria on Monday, which will be shown live on 

BBC One at 08:30 BST.

China are out of the competition after Denmark beat 

Haiti to finish in second place in Group D.

England prove worth without Walsh

Manager Sarina Wiegman kept everyone guessing when 

she named her starting XI, with England fans 

anxiously waiting to see how they would set up 

without injured midfielder Keira Walsh.

Wiegman's response was to unleash England's attacking 

talent on a China side who struggled to match them 

in physicality, intensity and sharpness.

James oozed magic and unpredictability, Hemp used her 

pace to test China's defence and captain Millie Bright 

was ferocious in her tackling, winning the ball back 

on countless occasions.

After nudging past Haiti and Denmark with fairly 

underwhelming 1-0 wins, England were keen to impose 

themselves from the start. Although China had chances 

in the second half, they were always second best.

Goalkeeper Mary Earps will be disappointed not to keep 

a clean sheet, but she made two smart saves to deny 

Chen Qiaozhu.

While England are yet to meet a side ranked inside 

the world's top 10 at the tournament, this will help 

quieten doubts that they might struggle without the 

instrumental Walsh.

"We're really growing into the tournament now," said 

captain Bright. "We got a lot of criticism in the first 

two games but we were not concerned at all.

"It's unbelievable to be in the same team as 

[the youngsters]. It feels ridiculous and I'm quite 

proud. Players feeling like they can express themselves 

on the pitch is what we want."

James given standing ovation

The name on everyone's lips following England's win 

over Denmark was 'Lauren James', and those leaving 

Adelaide on Tuesday evening will struggle to forget 

her performance against China any time soon.

She punished China for the space they allowed her on 

the edge of the box in the first half and could have 

had a hat-trick were it not for the intervention of VAR.

Greeted on the touchline by a grinning Wiegman, 

James was substituted with time to spare in the second 

half and went off to a standing ovation from large 

sections of the stadium.

"She's special - a very special player for us and 

for women's football in general," said Kelly. "She's 

a special talent and the future is bright."

She became only the third player on record (since 2011) 

to be directly involved in five goals in a Women's 

World Cup game.

With competition for attacking places in England's 

starting XI extremely high, James has proven she is 

far too good to leave out of the side and is quickly 

becoming a star at this tournament at the age of 21.


prompt = f"""

In the text indicated by three back ticks answer the \

following questions and output your answer as JSON \

using the key names indicated by the word key_name" \

1) What sport was being played? key_name="sport" \

2) What competition was it? key_name="competition" \

3) What teams were playing? key_name = "teams" \

4) Which team won? key_name = "winner" \

5) What was the final score? key_name = "score" \

6) How many people attended the match? key_name = "attendance" \



messages = [{"role": "user", "content": prompt}]

response = (openai







Here are the results this code produces:


  "sport": "Football",

  "competition": "Women's World Cup",

  "teams": "England and China",

  "winner": "England",

  "score": "England 5 - China 1",

  "attendance": 13497


This is mostly right, but not quite. The score was actually 6-1. Even worse, the results are very sensitive to the text layout; changing line breaks changes the score.

I ran the same query, but with the Guardian article instead and here's what I got:


  "sport": "football",

  "competition": "World Cup",

  "teams": "England and China",

  "winner": "England",

  "score": "6-1",

  "attendance": null


With a better prompt, it might be possible to get better consistency and remove some of the formatting inconsistencies. By analyzing multiple articles on the same event, it may be possible to increase the accuracy still further.


Sometimes ChatGPT gets it very wrong and supplies wildly wrong answers. We've seen a little of that with its analysis of the World Cup game, it wrongly inferred a score of 5-1 when it should have been 6-1. But ChatGPT can get it wrong in much worse ways.

I ran the queries above with text from the BBC and The Guardian. What if I ran the query with no text at all? Here's what I get when there's no text at all to analyze.


  "sport": "football",

  "competition": "World Cup",

  "teams": ["France", "Croatia"],

  "winner": "France",

  "score": "4-2",

  "attendance": "80,000"


Which is completely made up, hence the term hallucination.

Prompt engineering part 2

Let's go back to my elephant example from earlier and write it this way:

prompt = f"""

In the following text, "The kind old elephant", 

how often does the letter e occur



messages = [{"role": "user", "content": prompt}]

response = (openai







Here's what the code returns:

In the phrase "The kind old elephant," the letter "e" occurs 4 times.

Which is clearly wrong.

In this case, the problem is the placement of the text to be analyzed. Moving the text to the end of the prompt and being more explicit about what should be returned helps. Even simply adding the phrase "Give your answer as JSON" to the prompt fixes the issue.

This is why the precise form of the prompt you use is critical and why it may take several iterations to get it right.

What does all this mean?

The promise of ChatGPT

It is possible to analyze text and extract information from it. This is huge and transformative for business. Here are just a few of the things that are possible:

  • Press clippings automation.
  • Extraction of information from bills of lading.
  • Automated analysis of SEC filings.
  • Automated analysis of company formation documents.
  • Entity extraction.

We haven't even touched on some of the many other things ChatGPT can do, for example:

  • Language translation.
  • Summarization.
  • Report writing.

How to deliver on that promise

As I've shown in this blog post, the art is in prompt engineering. To get it right, you need to invest a good deal of time in getting your prompts just right and you need to test out your prompts on a wide range of inputs. The good news is, this isn't rocket science.

The skills you need

The biggest change ChatGPT introduces is skill levels. Previously, doing this kind of analysis required a good grasp of theory and underlying libraries. It took quite a lot of effort to build a system to analyze text. Not any more; the skill level has just dropped precipitously; previously, you needed a Ph.D., now you don't. Now it's all about formulating a good prompt and that's something a good analyst can do really well.

The bottom line

ChatGPT, and LLMs in general, are transformative. Any business that relies on information must know how to use them.