Data Analysis with LLMs: Text, tables, images and sound

Welcome to the Deep Dive, the show that navigates the labyrinth of information, distilling the essence of what truly matters. I vividly remember the first time I interacted with GPT three. Oh yeah, it felt like an almost magical experience. For the first time, it genuinely seemed like the computer understood my complex inputs and could react appropriately, you know, solving diverse tasks from text analysis to coding, just based on

It truly redefined what we thought was possible with AI.

The leap was just undeniable, absolutely, and today our Deep Dive is all about harnessing that power. We're going to explore how these incredible language models can be used specifically for data analysis, helping you make the most of your data sets. Right, They've evolved so rapidly, moving from just processing text to understanding multimodal inputs that's images, audio, video, and of course tech.

Yeah, the multi modality is huge.

This expansion makes them an invaluable tool across pretty much every facet of data science.

Our mission in this deep dive really is to show you how llms can act as expert guides to your data, offering a genuine shortcut to being well informed. A shortcut I like that will delve into how they extract the most important nuggets of knowledge and insight from various sources and empower you to build complex analysis pipelines with just a few lines of Python code, all driven by natural language instructions.

Okay, let's unpack the core of this magic. Then it all begins with what GPT actually stands for, generative pre trained transformer. Generative is key here, meaning these models don't just classify or recognize things. They create new content, whether it's text, code or even images.

And the pre trained aspect means they've learned from truly immense amounts of data, vast swaths of the Internet, books, and more, enabling them to understand languid broadly, not just you know, specific narrow task. This generic understanding allows them to then adapt to specialized tasks much more efficiently. And the transformer part, ah, that's the underlying neural network architecture, the brilliant design that makes all this possible.

So how does this fundamental design let them tackle such a wide array of problems.

What's truly fascinating is how this design allows lms to be universal task solvers. Unlike earlier models built for one specific purpose, llms are designed intended to serve as universal task solvers that can, in principle, solve any task the user desires.

Any task wow.

Well within reason. The way you communicate with them is through prompting. Think of a prompt as your direct instruction to the model. So the input you give it, and it can be multimodal, combining text with images or other data types. A really effective plompt needs a clear task description, all the relevant context like are we talking about reviewing laptops or lawnmowers for example? Right?

Context matter Context is.

Critical, and crucially, it can optionally include a few examples to guide the model.

So if the prompt is the key, how much handholding do we actually need to give the model? Does it learn from a few examples or can it just get it from the description alone?

Yeah, that brings us to fu shot learning versus zero shot learning. FU shot learning is when you provide those few examples directly in your prompt to show the model exactly what you expect showing your work exactly. It's like showing someone a couple of solved puzzles so they understand the pattern. Zero shot learning, on the other hand, means you're relying solely on your task description with no examples provided, and that works. It's impressive how often llms can still

And it's important to distinguish between the types of data lllms work with, right structured versus unstructured.

Absolutely, we have structured data that's your tables grabs, anything with a fixed format that specialized tools can process very efficiently. For this primarily act as an intelligent.

Interface, got it, like a translator kind of.

Then there's unstructured data text, images, audio video, where llms operate directly on the raw content. A critical point for anyone using these models, and something that often surprises people, is that interacting with language models incurs monetary fees. AH the cost yes proportional to the amount of data process and using larger language models, well that's often more expensive significantly, So sometimes how.

Do they measure that cost?

These costs are calculated in tokens. Think of tokens as the smallest, meaningful lego bricks of language. So if I say Hello World, that might be just a few tokens. It's roughly four characters a text, give or take.

That's a good way to put it. So for many of us are first let's say, dance with an LLM was likely through the chat GPT web interface. Ugly, most of you have probably already dabbled there, accessing it at chat dot OpenEye dot com. It's a great sandbox for quick text processing or even exploring its data analysis capabilities.

And in that web interface you can perform some genuinely practical tasks. For text processing, classification is straightforward determining the sentiment of a movie review or sorting a product review, like for a I don't know a banana book a banana book into its correct category. You can even hint it your desired output format simply by saying answer concisely.

Nice.

For information extraction, it's brilliant at pulling structured data from freeform text, like gathering a name, GPA, and degree from a stack of applicant emails.

And what's truly impressive is how it handles tables right right there in the chat.

It really is. You can upload a dot csv file, for instance, review stable dot csv. Chat SHPT doesn't just display it. If you've enabled to write features, it generates an executes Python code behind the scenes to analyze that data hikon code.

Really Yeah.

You can even peak at the code by clicking the show analysis button. This demonstrates lms acting as intelligent orchestrators for external tools.

Wow.

They also excel as translation, converting natural language questions into formal query languages like sql.

Ah SQL generation.

That's useful, very You can then execute that SEQL on your own platform, say and squally database in a Google collab notebook. It's fantastic for writing complex multiline queries, and that handy copy code button makes it so easy to grab the generated sequel.

That sounds incredibly powerful, but it also raises an important question. Can we truly trust everything? A LLLM tells us.

That's a crucial point, and it's one of the biggest challenges. The term hallucinations refers to situations where lms will invent new content in the absence of information, invent things. Yes, and the truly profound insight here isn't just that they invent things, but that they do so with such convincing confidence it sounds completely plausible.

Oh that's dangerous, it can be.

This fundamentally shifts our perspective, and LLLLM doesn't know in the human sense, it generates plausibly, Yeah, forcing us to rethink how we trust automated information. So it's essential to always verify the output, Always double check before relying on it. Use alternative sources for corroboration.

Okay, always verify.

Got it.

So, while the web interface is great for chatting and quick tasks, it's not really designed for building robust, complex data processing pipelines.

Not really.

No, for that, we need to go deeper into the code. This is where the open ai Python library comes into play.

Exactly. The Python library allows you to directly invoke llms as a subfunction within your own code, giving you much more programmatic control.

How do you get started with that?

To get set up, you'll need Python three point nine or later, and then simply install the opene library using pip standard stuff. Okay, critically, you'll need an API key from open Ai, and it is highly recommended to store this securely as an environment variable.

Right, don't just paste it in your.

Code, absolutely not. Never ever share your code if it contains your open AI access key directly, as others could use it to encour charges on your account. Very important.

Okay, key secured?

Then what when using check completetion in Python, you can struct a list of messages. Each message has a role user for your input, assistant for the model's reply or system for instructions about the model's persona or behavior.

System user assistant okay.

And then the actual content of the message the client dot chat, dot completions, dot create function handles setting this off. Remember token usage, specifically, the total tokens attribute in the response you get back directly impacts cost right back to the tokens, and tokens generated by the model are often more expensive than the tokens you send as input. Keep that in mind.

Ah, good tip. Now that we know how to talk to these models through code, the next logical step is how do we steer them, how do we make sure they behave exactly as we want, and crucially, how do we manage those costs.

That's where customizing model behavior and optimizing for costing quality comes in. It's really about controlling the generation process. Oh so, for example, to control output length and therefore fees, you can set max token to specify a maximum response length.

Pretty straightforward, can't limit the output makes sense.

You can also use obsequences specific text patterns like maybe endo response or even something narrative like and they lived happily ever after to tell the model exactly when to stop generating. This can be very useful for getting structured.

Outputs ah nat trick, and for controlling the actual words it chooses. How do we guide that for output generation?

Presence penalty and frequency penalty are your levers for controlling repetitiveness. Positive values discourage the model from repeating tokens it's already used or that are present in the prompt helps keep things fresh.

That's repetition.

Good for truly surgical precision, like forcing a model to use specific words, say positive or negative in a sentiment task. There's legit bias.

Legit bias sounds complex.

It's a more advanced lever. It lets you explicitly increase or decrease the likelihood of specific tokens appearing. You need to find the token IDs using a token aser tool first. It's powerful, but typically for very niche use cases. You wouldn't use it every day.

Okay, And what about controlling how creative or let's say random the model gets. Sometimes you want predictable, sometimes more exploratory.

That's where randomization parameters are key. Temperature, typically set between zero and two, directly controls randomness. Higher values like maybe point eight or one point zero lead to more diverse and sometimes more created outputs. Lower values closer to zero make it more deterministic and focused.

So zero for facts, higher for fiction.

Sort of kind of yeah. TOP is an alternative approach that achieves a similar goal. It reduces randomization by focusing only on the highest probability tokens that add up to a certain cumulative probability. It's just a different way to tune.

The randomness, temperature or TOP. Okay.

And if you want multiple options for a single plumpt, you can use the N parameter to generate several replies at once. Gives you more choices to pick from.

This raises an important question with all these settings, how do we get the best perform ormans while managing costs effectively? It sounds like a balancing act.

It absolutely is, and that's where strategic optimization becomes crucial first, model selection. Do not always default to the largest, most expensive available model.

Bigger isn't always better.

Not necessarily and certainly not always cost effective. For many simpler tasks, a smaller, cheaper model like GPT three point five turbo might perform perfectly well. GPT four, for instance, can be over one hundred times more expensive per token in.

Some cases, wow, a hundred times.

Yeah, it's smart to check benchmarks like Stanford's ALM evaluation and definitely experiment with different models for your specific task to find that sweet spot between cost and quality.

So model choice is clearly a big one. What else can we do besides tweaking temperature and penalties?

Prompt engineering is absolutely vital. I can't stress this enough. The design of your prompt can have a significant effect on.

Performance, really just the way you ask.

Yes, it's a really counterintuitive insights sometimes, but the biggest leap in performance might not come from a bigger model or more training data, but simply from better instructions, like a skilled artisan responding to a perfectly precise brief. You know. That's the magic of fu shot learning, which we mentioned earlier, including samples of correctly solved tasks directly in the prompt

So invest time in the prompt itself.

Definitely. You can even find ready made prompt templates on platforms like prompt Base, though crafting your own specific to your need is usually best.

And what about fine tuning? That sounds like a big step like retraining the model?

It is kind of. Fine tuning allows you to specialize base models to the specific tasks you care most about. You take an existing model like GBT three point five Turbo and you continue its training, but with a relatively small amount of your own task specific data, typically fifty to maybe a few thousand examples.

Fifty examples. That doesn't sound like much compared to the pre training data.

It's not, but it's focused. The model already understands language, you're just nudging it to be really good at your specific thing.

What are the upsides and downsides of that kind of specialization? Seems powerful?

The advantages include potentially significantly improved accuracy for your specific use case, and you might get away with shorter, simpler prompts. Because the task is sort of baked into the specialized model now and.

The downsides cost I assume yes.

There are upfront monetary fees for the training process itself, and importantly it usually increases the cost per token for the fine tuned model's ongoing usage compared to the base model.

Ah, so it costs more to run afterwards.

Often Yes, the training data also needs to be in a specific JSM lines format, basically representing successful interactions as little conversations with user and assistant roles. It's a powerful tool, but when you typically explore once you've exhausted prompt engineering options.

Okay, that makes sense. Let's unpack this further than beyond just text, lms are fundamentally transforming how we interact with all sorts of data, right, not just words on a.

Paid absolutely for text analysis. Classification remains a natural application, like we said categorizing movie reviews or support tickets. Information extraction, where you pull structured data like compiling a table of applicant attributes from free form emails, is another really strong suit.

Yeah, pulling structured data from messy texts is huge and.

For clustering which groups semantically similar text documents, llms leverage something called embeddings.

Embeddings heard that term, what is it exactly?

Think of embeddings like assigning every piece of text a unique invisible address in a vast high dimensional space, like a point on a complex map. Okay, the closer to addresses or points are on this map, the more similarly the meaning of the texts. This allows the computer to understand semantic similarity without actually reading in our human sense. It's purely mathematical, so.

It turns meaning into.

Coordinates basically, yes, and this makes tasks like clustering emails incredibly efficient, separating them from, say, poems. It also powers things like semantic search and retrieval systems find me documents like this one.

So it's about turning complex information into something that computer can intelligently compare and measure. That's genuinely mind.

Boggling precisely, and for structured data analysis think relational databases or graph databases. Lms truly act as a universal interface interface. How they translate your natural language questions directly into formal query languages like SQL for tables or cipher for graphs. This contrast sharply with the traditional need for someone to manually write those precise, often complex queries.

So I can just ask my database questions in English.

That's the goal. We often use external tools with the LM for this because of efficiency, cost, and the sheer volume of large data sets that would exceed in LM's input limits. The LM acts as the translator.

How does that work? In practice?

You can build a natural language query interface for tabular data, for example, by first having the LM automatically extract the database structure, maybe by querrying the quite master table in squite ah.

It figures out the tables and columns itself exactly.

Then it translates your natural language questions into SEQL queries based on that structure, and finally, your application executes those queries against the actual database.

That kind of automation sounds amazing, but with that level of power accessing databases directly, there must be a significant caution, right.

Yes, a big one, a huge one actually. Do not blindly trust your language model to generate accurate queries. They can make mistakes. What kind of mistakes They might misunderstand the question, misinterpret the schema, or generate SQEL that's inefficient or just plain wrong, or worse, potentially destructive if you've given it right access.

Yikes.

So always always keep a backup of important data before enabling data access via language models, and ideally have checks in place, maybe even human review for sensitive querities. It's power that absolutely needs human oversight.

Okay, creceed with caution on database access. Got it. And it's not just text and tables anymore. Llms are now analyzing images and videos too. How does that work?

It's truly incredible. Models like GPT four to H are natively multimodal. This means they were trained from the ground up on different types of data, not just text, so they can see in the sense, you could ask free form natural language questions directly about images. For example, detect golden persian cats in this picture, and you provide the image along with the text wow. Your prompts combine text

And cost? Is analyzing images expensive?

The cost is generally proportional to the resolution of the images you submit. High resolution, more detail, potentially more tokens used.

Okay, what about say, tagging people in photos.

Do that too. You could provide a reference picture of a person alongside the pictures you want to tag, using multimodal prompts with two or more images and text instructions like is the person in the first image present in the second image?

What if my images and videos aren't online if they're stored locally on my computer?

Good question. For local images or video frames, you need to encode them first. Common formats like PNG jpeg up to about twenty milibuni in size need to be converted into a text format called Base sixty four and then encoded as UTF eight.

Encode them as text yes.

Essentially turning the image data into a long string of characters that can be sent in the API request along with your text prompt. Libraries like OpenCV are commonly used to extract individual frames from videos, maybe just the first ten frames, to get a sense of the videos content and.

What would you do with those frames?

You could use those sampled frames, along with text instructions to say, generate a concise video title, like provide the frames and ask generate a short title for a video showing these scenes. It might come back with traffic conditions on I five during rush hour.

That's remarkable versatility taking us from text to tables, to images and video frames. And finally, what about audio data? Can they listen?

Be sure, can or at least process the data for audio data analysis. Open AI's Whisper model is a real game changer. Yeah, it's a transformer model like GPT, but train specifically on over six hundred and eighty thousand hours of multilingual audio data. It's excellent for transcription, converting audio recordings into written text, typically English text output, though it understands many languages.

So speech to text. What formats?

It supports common formats like MP three, WAV and others, usually with a file size limit around twenty five milibit for the standard API.

What can you build with that?

You could build a full voice query interface. Imagine record a spoken question using a library like sound device on your computer, OK, transcribe that audio to text using whisper, translate that text into a SQL query GBT four H, execute the query against your database, get the result, and then present the answer back as speech using text to speech generation.

Whoa a full voice assistant for your data exactly?

OpenAI also has a tts IE model for that text to speech part You can select from various voices, give it the text answer, and it generates the audio. Pricing for TTS is usually based on the number of characters you convert.

That's amazing. What about translation?

This whole pipeline also enables simultaneous translation. Effectively, spoken input in one language gets transcribed by whisper, translated to text in the second language by GPT, and then spoken aloud in that target language using the TTS model.

Wow, the pieces are all there. Okay, so we've covered the basics with open AI. But now for the part that truly blew my mind when I was researching this. The world of LMS extends far beyond just open AI.

Oh absolutely, It's a rapidly growing ecosystem that are prominent GPT alternatives, each with unique philosophies and strengths. Like COO, Well, there's anthropic. With their claud models. They emphasize a constitutional AI approach, trying to build models that are inherently helpful and harmless through their training process.

Constitutional AI. Interesting.

Then you have cohere. Their command R plus model, for instance, focus is heavily on grounding to avoid hallucinations, yet linking the model's answers back to real data sources. They alpha use techniques like RAG which stands for retrieval augmented Generation, and provide connectors that allow the model to perform web searches or query databases to find factual information before generating an.

Answer, So fact checking itself before answering.

That's the idea. And of course Google they played a foundational role by inventing the transformer architecture itself back in twenty seventeen. Their Gemini models offer very powerful multimodal capabilities as well.

Right, Google's a huge player, and for those who prefer more control, maybe running models locally on their own machines.

That's where hugging Face really shines. They are central to the open source AI community.

Hugging Face like the emoji.

Exactly like the emoji, they provide a vast platform and ecosystem for open source models. This allows users to download and run models on their own local infrastructure.

What's the benefit of that It can.

Be significantly cheaper in the long run, especially for high volume use as you're not paying per token to an API, And crucially, it's ideal for sensitive data that you absolutely cannot send to a third party API for privacy or security reasons makes sense.

How many models are we talking about?

The hugging face hub has? I think over a million models, data sets and related resources. Now, it's enormous. You can filter models by task, text classification, image generation, visual question answering, you name it. It's a real treasure trove for finding or building custom solutions.

A million models. Wow? Okay. So for those truly complex, multi step data analysis pipelines we talked about earlier, maybe combining database queries with web searches and tech summarization. Yeah, we're talking about next level tools, right frameworks exactly.

For those kinds of sophisticated workflows, you'll often turn to software frameworks like lang chain and lama index. They help manage the complexity.

Lang chain, what's the core idea there?

Lang chain helps you compose complex applications through chains. Think of chains as ways to sequence operations, integrating LMAM calls with other standard Python functions or external.

APIs like linking steps together precisely.

Key components include things like chat prompt template for creating reusable prompt structures, chat open ai or similar for making the actual API calls to the LLM, and strout pot parser for neatly getting the text result out.

Okay, building blocks.

But the really powerful concept in lang chain is often agents.

Agents like secret agents.

Yeah, sort of think of an agent as putting the LLM in the driver's seat. You give it a complex task and a sit of tools that can use.

Tools.

Being tools are basically just regular functions, Python functions, API calls, whatever, but they have a natural language description telling the agent what the tool does. The agent can then look at the tag, break it down and decide Okay, for this part, I need to use the sql query tool. For that part, I'll use the web search tool. Then I'll use the summarizer tool. It figures out the plan and wish tools to invoke in what order.

So the LLM itself orchestrates the workflow using the tools you give it exactly.

For instance, you could build a data analysis agent that combines several relational database tools one to list tables, one to get the schema, one to check sql syntax, one to actually run the query, maybe alongside a web search tool for pulling in external context. You define these custom tools quite easily, often using a simple a tool decorator.

In Python that sounds incredibly powerful. Giving the LM agency to solve problems and LAMA index how does that fit in?

Lamma index is another extremely popular framework, and it particularly excels when you're dealing with large collections of your own data, especially documents of various types of PDFs, powerpoints, text files, etc.

So more focused on query and your own stuff.

Yes, its main strength is in how it indexes this data. It pre processes your documents to create efficient search structures, often using those embedding vectors we talked about earlier. This allows it to quickly identify the most relevant subsets of your data for a given query.

Ah, So it finds the right needle in the haystack first, exactly.

It retrieves the most relevant chunks of information and then passes only that context, along with your question to a powerful LLM like GPT four to synthesize the final answer. This is much more efficient and effective than trying to stuff entire documents into an LM prompt, which often isn't even possible due to length limits.

So lemmy index is about retrieval, augmented generation, finding the relevant bits first.

Precisely, you could build a simple question answering system over a whole directory of diverse company reports, PDFs, powerpoints, maybe web pages, and Lemmy index handles fetching the right pieces of info before the ll generates the answer.

So how do they compare lang Chain and lemnx. Are they competitors or complementary? Do you use one or the other?

That's a great question. Lane Chain is perhaps a more general framework for build all sorts of LLM powered applications, with a strong focus on agents and chains orchestrating tools. Lemmy Index is more specialized, really focus on that interaction pattern between lms and large external data sets via indexing and retrieval.

So different focuses, right.

You'll often see them used together. Actually, you might use law index to build a robust retrieval tool and then incorporate that tool into a Lange chain agent, or you might use one over the other, depending on whether your main challenge is complex orchestration, lang chain or querying large knowledge bases bamby index. Both frameworks are also relatively young and evolving incredibly quickly, so the lines can blur. It's really exciting space to watch.

So what does this all mean for you, the listener? We've unpacked how llms are not just for generating text or chatting, but are becoming these incredibly powerful engines for a data analysis across pretty much every format imaginable.

Yeah, it's gone way beyond chatbots.

We've covered their incredible versatility, some practical techniques for optimizing cost and performance, and explore these advanced frameworks like lang chain and lem index that let you build truly sophisticated applications. We're really just scratching the surface of what these frameworks can do, but the key takeaway is they unlock true programmatic power for llms.

And remember, knowledge is really most valuable when it's understood and applied. We encourage you to think about the practical applications of this knowledge in your own fields, your own work, whether that's automating the analysis of customer reviews, building voice interfaces for your internal tools, or extracting new insights from complex multimodal data sources. The possibilities are just vast. Now, this really raises an important question, maybe the most important one.

That's the real question. And while llms definitely offer an almost magical experience. Sometimes, maybe the true power lies in our human ability to understand how they work warts and all, to manage their limitations, like those convincing hallucinations, and to critically design the prompts, the workflows, the frameworks that turn all that raw data into meaningful, verified insights.

Verification is key.

The continuous journey of learning and applying these evolving tools, figuring out how to use them responsibly and effectively. That really feels like the most exciting frontier in data right now.