# LLM Block

The **LLM Block** in GLIK Studio is a node that routes data into a large language model (LLM) — such as OpenAI's GPT or Anthropic's Claude — and returns a generated response. It enables natural language understanding, reasoning, and synthesis to be embedded inside a structured enterprise workflow.

It acts as a dynamic reasoning node for:

* Decision explanation
* Ambiguity resolution
* Freeform synthesis (e.g. summaries, justifications, answers)

***

### 🧱 Block vs. Node in GLIK

* A **block** is a configurable building element in GLIK Studio's visual interface.
* A **node** is an instance of that block placed into a specific workflow (graph).

Think of a **block** as a type or template — and a **node** as its usage in a particular context.

***

### 🌐 GLIK Studio vs. GLIK Cloud

* **GLIK Studio** is the visual builder for composing workflows using blocks (like LLM Block).
* **GLIK Cloud** is the managed runtime environment where workflows are executed — it runs and scales your AI-powered applications.

Workflows designed in Studio are published to Cloud.

***

### 💼 Enterprise Use Case Context

The LLM Block allows enterprise builders to embed generative reasoning into business logic. It becomes especially useful in:

#### For Enterprise Developers

* **Fallback logic**: Handle uncertain or missing cases in rules-based flows.
* **Complex prompt chains**: Inject variables and memory into prompts to drive dynamic reasoning.
* **Human-like summarization**: Explain a decision using natural language.

#### For Executive Decision Makers

* **Transparency**: LLMs explain how and why an approval was made.
* **AI as a co-pilot**: Use LLMs to evaluate nuanced decisions based on enterprise context.
* **Policy compliance**: Summarize which thresholds, rules, or values were triggered.

***

### 🛠 Common Configurations

| Property       | Description                                           |
| -------------- | ----------------------------------------------------- |
| Prompt Input   | The system message or instruction for the LLM         |
| Variables Used | Pulls from upstream nodes or session variables        |
| Model Target   | Can be set to a default (e.g., GPT-4, Claude)         |
| Output Binding | Stores output in a variable like `conversation_notes` |

***

### 📘 Example Usage

> In the **Expense Policy Decision Engine**, the LLM Block is triggered when invoice data is incomplete or ambiguous. It generates an explanation and decision recommendation such as:\
> \&#xNAN;*"Based on the vendor history and lack of an uploaded policy document, this expense should be escalated for manual review."*

***

### 🧠 Best Practices

* Always use **fallback conditions** to avoid over-relying on the LLM
* Keep prompts **deterministic and structured** if used in workflows that affect finance, security, or compliance
* Include **memory injection** if context across steps is important

<details>

<summary>In Progress</summary>

This page is currently under active development. Content may be incomplete, evolving, or placeholder-only. Please check back later for finalized documentation and fully structured examples.

</details>

### 🚀 Looking Ahead

Future versions of GLIK will support:

* Fine-tuned models per organization
* Prompt versioning and audit logs
* Role-based prompt injection for use-case control


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