LLM In-Context Learning Masterclass feat My (r/reddit) AI Agent

A Reddit AI agent can be made to deliver on-topic, “in-style” answers by stuffing a carefully curated “brain” into the model’s prompt—combining a knowledge base, rules, and example good/bad responses—then pairing that prompt with targeted Reddit search and optional scraping of linked media. The core insight is that the hardest part isn’t the Reddit automation; it’s building the prompt context that reliably steers the model toward useful, policy-safe, and recognizable commenting behavior.

The system starts by choosing the topics the agent should reliably handle. In this build, those topics include specific model and ecosystem keywords such as “Claude 3,” “AI agent,” “Mistral 7B,” “Llama Index,” and “LangChain,” alongside hardware and tooling terms like “Nvidia Blackwell,” “H100,” and “B200.” For each topic, the builder creates “brain content” that functions as a prompt-fed knowledge base. It includes (1) factual snippets about models and their differentiators, context windows, and pricing; (2) rules the agent should follow; (3) example post-and-comment pairs that demonstrate the desired response length and tone; and (4) intentionally “bad examples” to discourage unwanted behavior.

That “brain” is then fed into a large in-context prompt alongside the live Reddit material. The prompt grows quickly: a pasted draft lands around 9,000 tokens per API request because it bundles the knowledge base, rules, example pairs, and the fetched Reddit post fields (title and content). The build notes that this can become expensive with larger models like Claude Opus or GPT-4-class options, but remains manageable when using cheaper models and only posting occasionally.

On the automation side, the agent searches specific subreddits (tested with r/singularity, r/openai, and r/local llama) for posts whose titles match selected keywords. When a post includes a URL or an image with little text, the system scrapes the link and uses Claude 3’s image capabilities (via the Sonnet API) to generate an image description, which is then incorporated into the prompt so the model can still respond with context.

The code also includes practical guardrails to prevent spam. It waits a random interval between 30 and 60 minutes (with plans to extend to 60–120 minutes) so comments land infrequently. More importantly, it stores “replied post” IDs and reloads them on restart, ensuring the agent doesn’t comment multiple times on the same thread—an issue that previously caused repetitive posting.

Finally, the agent generates comments in a lower-case style and matches the length and structure of the example replies. Live tests show it can produce substantive answers, such as discussing Claude’s tool-use capabilities and offering hardware guidance for local LLM setups (e.g., RTX 3090 vs 4070 VRAM tradeoffs). The builder also embeds subtle promotion for the “All About AI” YouTube channel inside the response style, using the same prompt framework to keep that messaging consistent rather than intrusive.

Overall, the project frames “in-context learning” as an engineering task: collect and structure domain data, curate examples, and then let the model do the writing—while the surrounding system handles retrieval, media extraction, rate limiting, and deduplication.