Summary 1. **RAG (Retrieval Augmented Generation):** Augmenting the knowledge base (db) to enhance responses by combining language models. 2. **Chain of Thoughts:** Promoting ideas using 'thoughts' 💭 in the form of chunks one by one to obtain actual answers. Language models arrive at your desired answers through reasoning and logic. 3. **ReAct (Thought, Action, and Observation):** Different from the chain of thoughts, this involves both private knowledge base (db) and public language model (llm) data. If information isn't in the knowledge base, it goes back to the public llm data (trained data) for results. 4. **DSP (Direct Stimulus Prompting):** The latest method involves hinting the prompt with a specific hint to get the answers.

@IBM Technology I got the theory but I want to see an example of the actual resulting prompt in each of the 4 methods. Nothing beats learning by example

So simple and focused on the main idea and key points. Thank you for your straightforward explanation!

I like they are labeling every interaction with the LLMs. Prompt engineering, rag, cot, react, dsp. These are the basic blocks and as a developer I share what many are already seen and working on it. A higher programing language where it is no longer constrained to direct the physical and structured layer to compute the results. This programming language will skip those layers 100% to work directly on the business problems. It may be named as mayeutic. Supporting in a new way critical questions; fast no longer will be the measure. Fast will be just side effect. The key will be the transition from RDBs, repositories, rudimentary data input, rudimentary finance procedures, to the next abstractions that would facilitate this smart agility using it.

I understand the main idea, but I think the examples and explanations weren't clearly thought through and felt vague. I didn't get a clear sense of how to apply these techniques effectively in real-life situations. However, I appreciate the intention and the effort put into it.

simple, direct, and on point. Thx a ton

A prompt is a specific instruction or query given to an LLM (Large Language Model) to perform a task. A task can be: Providing information, summarizing, analyzing, planning, reasoning, coding, generating, etc. Effective prompt engineering involves iteratively refining these instructions or questions to achieve a more accurate, relevant, or desired outcome from the LLM.

Absolutely amazing

Beautiful explanation!!

Love you guys

Really nice 👍

That amazing

Does this apply to all practical language models currently? This is how I should rizzz up my chat4 bot?

Nice, short clip, explaining such mega-areas in 12 minutes

Woule be helpful if you can come up with realime example and usuage. May be in parts..

How it is generating responses if I only have to train it with all actual data.

Nice video, thanks guys! Quick question: are all your engineers at IBM left-handed? You seem to have a bias for left-handed engineers 😅

@daveqr

4 ай бұрын

The image is reversed. They have a video explaining how they make lightboard videos.

@yesblahblah

4 ай бұрын

The view you are seeing it from has been flipped. There is a video on this channel or steve brutons where they explain how they make these videos. Also, if you assume the rule of wearing your wedding band on your left hand ring finger applies then you are looking at the marker being in his right hand.

@ethanfogarty9540

4 ай бұрын

They are all right-handed. The camera is behind them and is recording them facing and writing on some sort of mirror that makes their markers glow. Almost like an old school SmartBoard, but as a mirror.

It is wild to me how engineers view the research process. Honestly, they make it more complicated than it needs to be.

Im sorry I don't get it at all? What does the computer do exactly ?

@j.maginnenu6291

3 ай бұрын

Lol

Excellent presentattion

Wish you had show more specific examples

The lady explanation always confused me, but still appreciate the intention.

Good short/focused content. But example/context could have been lot better.

So ReACT is just RAG with two databases?

So basically this about ‘structuring’ your prompts in a way the LLM has to process your input…? And who is expected to formulate these ‘natural language’ questions…?

But but they all sound the same essentially? Please tell me the nuanced difference between the four.

sorry for my slowness. but the only thing I could understand is the RAG. the other ones are not clear.

She's good 👍

example prompts would've been helpful

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I have two questions. One, is IBM going to "decouple" from any dependency or vulnerability via China? Two, could IBM get back into the PC market? They were in rough times when they divested from their old PC, and sold it off as Lenovo. But they could really bring a high-end machine to market, and keep it U.S. developed.

@WilliamStonerock

4 ай бұрын

The US dependency by IBM is also problematic. The pervasive and unethical spying by the American govt should have any company that relies on AI worried.

😮Giving the same example for dsp and cop makes it confusing React isnt helping with prompt but with results... Misleading title

Instantly confusing and unclear. The example didn't even flow in relation to what she was saying.

That was confusing due to inferior examples given. No you didn’t succeed in explaining to a 8 year old

This is really terrible! RAG is not a method of prompt engineering, it's an architecture! And as far as the prompt explanations, they are also really poor. No wonder nobody uses IBM anymore

@made432

3 ай бұрын

@made432

3 ай бұрын

Awful video for beginners

Really bad examples, couldn’t they ask the AI to give better ones?

i never even thumb downed a video before. content was lacking. no prompt examples.

@j.maginnenu6291

3 ай бұрын

Lol😂

@galengkm

3 ай бұрын

Agreed this was very lame, terrible examples and explanation lacking in specificity and clarity

@app8414

3 ай бұрын

It's click bait.

@AdamPippert

2 ай бұрын

These videos are not for AI engineers, they are for business people that need to understand the tools and techniques used in generative AI. If you want real media AI content, go check out machine learning Street talk. This is not the channel for you.

This is completely inaccurate and confusing. IBM should take this down and check for accuracy of their content before putting this out there

@WilliamStonerock

4 ай бұрын

Can you outline, briefly, the inaccuracy?

@2010RSHACKS

4 ай бұрын

They trying to simplify it

I'm confused. Isn't RAG and prompt engineering two fundamentally different concepts?