Meta AI and Llama models reach client campaigns in two ways. Inside Meta’s ad platforms, AI features such as Advantage+ campaigns and generative creative tools shape targeting, placements and ad variations. Outside them, Meta’s openly available Llama models can run on private infrastructure for text tasks where client data must stay in a controlled environment. Both need clear rules, tests and human review.
What is the difference between Meta AI and Llama?
Meta AI is the consumer assistant built into WhatsApp, Instagram, Facebook, Messenger and Meta’s glasses, and it is powered by Meta’s Llama family of models. Llama models themselves are released with openly downloadable weights under Meta’s own licence, which allows commercial use with some conditions. They can be run on your own servers, through cloud providers such as AWS, Azure and Google Cloud, or through hosting services.
For advertisers, a third piece matters most: the AI built into Meta Ads Manager. That includes Advantage+ campaign automation, Advantage+ audience, generative features such as background generation, image expansion and text variations, and machine learning that decides delivery across Facebook, Instagram, Messenger and Audience Network.
How Alien Road applies Meta AI tools in paid social
Our paid social work follows a set process, and Meta’s AI features slot into specific steps.
Discovery
We review the client’s pixel and Conversions API setup, event quality, catalogue health for e-commerce, past creative performance and audience history. Meta’s automation depends heavily on clean conversion signals, so event match quality and deduplication between pixel and server events get checked first.
Campaign setup with Advantage+
For e-commerce clients with good signals, we usually test Advantage+ sales campaigns against a manual structure. For lead generation, we test Advantage+ audience with suggestions, such as past converters, against tighter interest targeting. Each test runs with a defined budget split and duration, so the comparison is fair.
Generative creative features
Ads Manager can generate text variations, expand images to fit different placements and create backgrounds for product images. We use them selectively.
- Text variations are reviewed before launch; we switch off automatic variations if they change claims or tone.
- Image expansion is checked on each placement, since stretched or invented edges sometimes look wrong.
- Background generation is used for lifestyle tests, never in a way that changes the product itself.
Measurement
We compare cost per result, conversion value and, where possible, incrementality through Meta’s conversion lift studies or geographic holdouts for larger budgets. Platform-reported results are cross-checked with GA4 and the client’s CRM or store data.
How do we use Llama models?
Open models are useful when data control matters more than having the strongest model. In a typical case, we run a Llama model inside a private cloud environment for tasks such as:
- classifying thousands of customer reviews by topic and sentiment for creative research;
- tagging a product catalogue with attributes for better ad feed titles;
- summarising internal campaign reports without sending them to an external chat service;
- drafting first versions of ad copy variations from approved product data.
The data stays within an environment the client or we control, and no prompts are used to train a third-party model. Outputs still go through human review, because smaller open models make mistakes, especially in Turkish and other less common languages.
Why not always use the open model?
Running models has its own costs: hosting, monitoring, updates and engineering time. For many tasks, a managed service with business data protection is simpler. We choose per task, based on data sensitivity, volume and the quality needed.