# ⭐ Core Concepts ## Glossary General and 🦑 _TruLens_-specific concepts. - `Agent`. A `Component` of an `Application` or the entirety of an application that provides a natural language interface to some set of capabilities typically incorporating `Tools` to invoke or query local or remote services, while maintaining its state via `Memory`. The user of an agent may be a human, a tool, or another agent. See also `Multi Agent System`. - `Application` or `App`. An "application" that is tracked by 🦑 _TruLens_. Abstract definition of this tracking corresponds to [App](/content/reference/trulens/core/app/#trulens.core.app.App). We offer special support for _LangChain_ via [TruChain](/content/reference/trulens/apps/langchain/#trulens.apps.langchain.TruChain), _LlamaIndex_ via [TruLlama](/content/reference/trulens/apps/llamaindex/#trulens.apps.llamaindex.TruLlama), and _NeMo Guardrails_ via [TruRails](/content/reference/trulens/apps/nemo/#trulens.apps.nemo.TruRails). `Applications` as well as custom apps via [TruBasicApp](/content/reference/trulens/apps/basic/#trulens.apps.basic.TruBasicApp) or [TruApp](/content/reference/trulens/apps/app/#trulens.apps.app.TruApp), and apps that already come with `Trace`s via [TruVirtual](/content/reference/trulens/apps/virtual/#trulens.apps.virtual.TruVirtual). - `Chain`. A _LangChain_ `App`. - `Chain of Thought`. The use of an `Agent` to deconstruct its tasks and to structure, analyze, and refine its `Completions`. - `Completion`, `Generation`. The process or result of LLM responding to some `Prompt`. - `Component`. Part of an `Application` giving it some capability. Common components include: - `Retriever` - `Memory` - `Tool` - `Agent` - `Prompt Template` - `LLM` - `Embedding`. A real vector representation of some piece of text. Can be used to find related pieces of text in a `Retrieval`. - `Eval`, `Evals`, `Evaluation`. Process or result of method that scores the outputs or aspects of a `Trace`. In 🦑 _TruLens_, our scores are real numbers between 0 and 1. - `Feedback`. See `Evaluation`. - `Feedback Function`. A method that implements an `Evaluation`. This corresponds to [Feedback](/content/reference/trulens/core/#trulens.core.Feedback). - `Fine-tuning`. The process of training an already pre-trained model on additional data. While the initial training of a `Large Language Model` is resource intensive (read "large"), the subsequent fine-tuning may not be and can improve the performance of the `LLM` on data that sufficiently deviates or specializes its original training data. Fine-tuning aims to preserve the generality of the original and transfer of its capabilities to specialized tasks. Examples include fine-tuning on: - financial articles - medical notes - synthetic languages (programming or otherwise) While fine-tuning generally requires access to the original model parameters, some model providers give users the ability to fine-tune through their remote APIs. - `Generation`. See `Completion`. - `Human Feedback`. A feedback that is provided by a human, e.g. a thumbs up/down in response to a `Completion`. - `In-Context Learning`. The use of examples in an `Instruction Prompt` to help an `LLM` generate intended `Completions`. See also `Shot`. - `Instruction Prompt`, `System Prompt`. A part of a `Prompt` given to an `LLM` to complete that contains instructions describing the task that the `Completion` should solve. Sometimes such prompts include examples of correct or intended completions (see `Shots`). A prompt that does not include examples is said to be `Zero Shot`. - `Language Model`. A model whose task is to model text distributions typically in the form of predicting token distributions for text that follows the given prefix. Proprietary models usually do not give users access to token distributions and instead `Complete` a piece of input text via multiple token predictions and methods such as beam search. - `LLM`, `Large Language Model` (see `Language Model`). The `Component` of an `Application` that performs `Completion`. LLMs are usually trained on a large amount of text across multiple natural and synthetic languages. They are also trained to follow instructions provided in their `Instruction Prompt`. This makes them general in that they can be applied to many structured or unstructured tasks and even tasks which they have not seen in their training data (See `Instruction Prompt`, `In-Context Learning`). LLMs can be further improved for rare/specialized settings using `Fine-Tuning`. - `Memory`. The state maintained by an `Application` or an `Agent` indicating anything relevant to continuing, refining, or guiding it towards its goals. `Memory` is provided as `Context` in `Prompts` and is updated when new relevant context is processed, be it a user prompt or the results of the invocation of some `Tool`. As `Memory` is included in `Prompts`, it can be a natural language description of the state of the app/agent. To limit the size of memory, `Summarization` is often used. - `Multi-Agent System`. The use of multiple `Agents` incentivized to interact with each other to implement some capability. While the term predates `LLMs`, the convenience of the common natural language interface makes the approach much easier to implement. - `Prompt`. The text that an `LLM` completes during `Completion`. In chat applications. See also `Instruction Prompt`, `Prompt Template`. - `Prompt Template`. A piece of text with placeholders to be filled in in order to build a `Prompt` for a given task. A `Prompt Template` will typically include the `Instruction Prompt` with placeholders for things like `Context`, `Memory`, or `Application` configuration parameters. - `Provider`. A system that _provides_ the ability to execute models, either `LLM`s or classification models. In 🦑 _TruLens_, `Feedback Functions` make use of `Providers` to invoke models for `Evaluation`. - `RAG`, `Retrieval Augmented Generation`. A common organization of `Applications` that combine a `Retrieval` with an `LLM` to produce `Completions` that incorporate information that an `LLM` alone may not be aware of. - `RAG Triad` (🦑 _TruLens_-specific concept). A combination of three `Feedback Functions` meant to `Evaluate` `Retrieval` steps in `Applications`. - `Record`. A "record" of the execution of a single execution of an app. Single execution means invocation of some top-level app method. Corresponds to [Record](/content/reference/trulens/core/schema/record/#trulens.core.schema.record.Record). Note This will be renamed to `Trace` in the future. - `Retrieval`, `Retriever`. The process or result (or the `Component` that performs this) of looking up pieces of text relevant to a `Prompt` to provide as `Context` to an `LLM`. Typically this is done using `Embedding` representations. - `Selector` (🦑 _TruLens_-specific concept). A specification of the source of data from a `Trace` to use as inputs to a `Feedback Function`. This corresponds to [Lens](/content/reference/trulens/core/utils/serial/#trulens.core.utils.serial.Lens) and utilities [Select](/content/reference/trulens/core/#trulens.core.Select). - `Shot`, `Zero Shot`, `Few Shot`, `-Shot`. `Zero Shot` describes prompts that do not have any examples and only offer a natural language description of the task to be solved, while `-Shot` indicate some `` of examples are provided. The "shot" terminology predates instruction-based LLMs where techniques then used other information to handle unseen classes such as label descriptions in the seen/trained data. `In-context Learning` is the recent term that describes the use of examples in `Instruction Prompts`. - `Span`. Some unit of work logged as part of a record. Corresponds to current 🦑 [RecordAppCallMethod](/content/reference/trulens/core/schema/record/#trulens.core.schema.record.RecordAppCall). - `Summarization`. The task of condensing some natural language text into a smaller bit of natural language text that preserves the most important parts of the text. This can be targeted towards humans or otherwise. It can also be used to maintain concise `Memory` in an `LLM` `Application` or `Agent`. Summarization can be performed by an `LLM` using a specific `Instruction Prompt`. - `Tool`. A piece of functionality that can be invoked by an `Application` or `Agent`. This commonly includes interfaces to services such as search (generic search via Google or more specific like IMDB for movies). Tools may also perform actions such as submitting comments to GitHub issues. A `Tool` may also encapsulate an interface to an `Agent` for use as a component in a larger `Application`. - `Trace`. See `Record`.