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Meetup Parameters

This is based on organising GenerativeAI Meetups in Bengaluru, India. This is a living document and will be updated as we learn more.

Venue

  1. Date & Time & Duration: Choose suitable timing and duration. Pick something that works for your community. Consider weekday and weekend meetups both.
    • Example: 4:00-5.00pm start on a Saturday works great in BLR! Chennai did a GenAI meetup on Saturday morning -- since that city wakes up early, it worked well for them.
  2. Camera: Consider the requirements for A/V if planning to do talks, streaming, or recording? Who is going to record? What kind of camera do we need? GenerativeAI outsources this to Hasgeek. Camera set-up for meetups is reduced to iPhone capture, with a Pivo Pod and tripod.
  3. Format: Define the structure of the meetup - is it just for drinks? Will there be talks? Is food provided? Is it open or by invitation only? Are plus ones allowed? Examples: GenerativeAI has never served alcohol, we often have 1-3 talks with snacks and is open by invitation only. We also have a Code of Conduct that we share with attendees.
  4. Speakers: If there will be talks, secure 1-3 speakers in advance. It's also fine to have a meetup with no speakers.
  5. Security: Keep in mind some venues might require pre-registration for security. Different venues enforce security with varying degrees of strictness. Some venues didn't allow anyone without registration, while the other venues allowed folks we didn't want to attend to enter. Discuss this upfront with your venue's security incharge.

Theme

  • Select a theme. This is crucial as it shapes the shared identity of all attendees and influences the discussions they initiate with strangers
  • Narrower themes are better than wider ones e.g. "DevTools" is better than "Enterprise tools for Devs"
  • Choose a catchy name to attract more attendees -- your naming is the most important branding
  • There are icebreaker lists on the Internet which you can use for more intimate meetings
  • Name tags: I've tried to enforce these, but failed at the GenerativeAI Meetups -- they're great! I've seen them work well at other meetups.

Photo and Video Documentation

  • Encourage attendees to take photos, tag you, or send them to you for sharing on social media
  • Consider recording the talks and posting them on YouTube afterwards to provide value to the community and allow great talks to live on. We use Hasgeek for this.
  • Note that live-streaming is not a must, but if done, it can add an extra layer of engagement for those who can't attend in person.
  • in careers
  • 2 min read

Function, Industry, Geography: Career Framework

Your career is a combination of Function, Industry, Geography.

That's it.

That's the framework.

You can change one of these. Not all three at a time.

Why only one at a time?

If you want to change your function, you need to learn new skills.

If you want to change your industry, you need to understand the new industry and the skills required to be successful in it.

If you want to change your geography, you need to uproot your life and move to a new place.

But what if I want to change all three?

You can, but it's difficult and perhaps needs a lot of thinking cycles and internal conviction.

Cheat code: Higher Education

MS/PhD/MBA helps folk change 2 of these at a time:

  1. You might get into a PhD program which changes your function and industry both
  2. MS abroad program which changes your function and geography
  3. MBA program which changes your industry (e.g. IT services to consulting) and geography (e.g. India to US)

Clarity Helps a Lot

The more granular you get, the easier it is to decide convert the wants into actionable steps.

Painful: I want to be a Machine Learning Engineer.

Tolerable: I want to be a Machine Learning Engineer at a Big Tech company.

Acceptable: I want to be a Machine Learning Engineer at Google.

Good: I want to be a Machine Learning Engineer at Google in New York.

Great: I want to be a Machine Learning Engineer working on the problems around generating human-like speech at Google in New York.

What if I am not clear about what I want?

If you are not clear about what you want, that's totally fine.

You can start with the most granular level and work your way up.

Start with the job description of the role you want. Talk to at least 12 people who are in that role.

Ask them what they do on a day-to-day basis. Ask how they got to where they are today. Ask what they ask when they are hiring or interviewing.

People on the Internet call this thing informational interviews and there's plenty of decent advice out there.

  1. The Antidote to I'm Feeling Stuck? from Swanand
  2. Act Like You're 35 from Nirant

Retrieval Augmented Generation Best Practices

Retrieval and Ranking Matter!

Chunking

  1. Including section title in your chunks improves that, so does keywords from the documents
  2. Different token-efficient separators in your chunks e.g. ### is a single token in GPT

Examples

  1. Few examples are better than no examples
  2. Examples at the start and end have the highest weight, the middle ones are kinda forgotten by the LLM

Re Rankers

Latency permitting — use a ReRanker — Cohere, Sentence Transformers and BGE have decent ones out of the box

Embedding

Use the right embedding for the right problem:

GTE, BGE are best for most support, sales, and FAQ kind of applications.

OpenAI is the easiest for Code Embedding to use.

e5 family does outside English and Chinese

If you can, finetune the embedding to your domain — takes about 20 minutes on a modern laptop or Colab notebook, improves recall by upto 30-50%

Evaluation

Evaluation Driven Development makes your entire "dev" iteration much faster.

Think of these as the "running the code to see if it works"

Strongly recommend using Ragas for something like this. They've Langchain and Llama Index integrations too which are great for real world scenarios.

Scaling

LLM Reliability

Have a failover LLM for when your primary LLM is down, slow or just not working well. Can you switch to a different LLM in 1 minute or less automatically?

Vector Store

When you're hitting latency and throughput limits on the Vector Store, consider using scalar quantization with a dedicated vector store like Qdrant or Weaviate

Qdrant also has Binary Quantization which allows you to scale 30-40x with OpenAI Embeddings.

Finetuning

LLM: OpenAI GPT3.5 will often be as good as GPT4 with finetuning.

Needs about 100 records and you get the 30% latency improvements for free

So quite often worth the effort!

This extends to OSS LLM models. Can't hurt to "pretrain" finetune your Mistral or Zephyr7B for $5