Featured
Why agentic AI is the next shift
Agentic AI plans, uses tools, and continues a job. A chatbot only answers. In AshnaAI, agents get a spec, tools, and permissions instead of a giant prompt.
Glossary
What is RAG, hallucination, and prompt engineering
AI terms that attract search and answer-engine traffic: what is RAG, what is hallucination, what is prompt engineering, what is an AI agent.
These pages exist so a search for what is RAG or why models hallucinate can land on an AshnaAI how-to, not only a dictionary.
More articles
Blog
Prompt patterns: chain of thought
Chain of thought means asking the model to work in steps. In AshnaAI, prefer a spec plus tools over a long 'think step by step' paste.
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Chunking strategies for RAG
Chunking splits a file so retrieval can find the right passage. In AshnaAI, upload the file and wait for embeddings. You do not pick chunk sizes in the how-to path.
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Confidence-based escalation design
A confidence score is a signal to ask a human. In AshnaAI, Tally and other writes should pause when the row is unclear instead of posting a guess.
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Context windows explained
A context window is how much text a model can see at once. In AshnaAI, do not paste a 100-page PDF. Attach the file and retrieve.
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Designing conversational flows
A conversational flow is the path a user takes. In AshnaAI, keep the first agent to one job, then embed or schedule after the chat path is clean.
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Semantic search vs keyword search
Keyword search matches words. Semantic search matches meaning. AshnaAI file chat uses embeddings. Slack and Gmail search use the provider APIs.
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Embeddings in plain English
Embeddings turn text into numbers so similar passages sit near each other. AshnaAI embeds uploaded files so chat can retrieve them.
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Fine-tuning vs RAG
Fine-tuning changes model weights. RAG retrieves your files at ask time. AshnaAI how-tos use RAG and tools. You do not fine-tune a model to chat with a PDF.
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Foundation models and multi-model platforms
A foundation model is a general model you adapt with prompts, tools, and retrieval. AshnaAI is a multi-model platform with X1 as the default router.
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Generative AI use cases that ship fast
Fast AshnaAI use cases are file jobs, email drafts, Slack recaps, and one-job agents. Avoid a first project that tries to run the company.
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GPT in production: prompting tips
In AshnaAI you can pin a GPT catalog row, but production reliability comes from tools, confirms, and files—not a longer prompt.
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A grounding checklist for AI agents
To cut hallucinations, attach the source, ask for quotes, and refuse to answer when the file is missing. AshnaAI retrieves. It does not train on your PDF.
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Building guardrails for agents
Guardrails are permissions, confirms, and tool limits. In AshnaAI the builder asks for connector access before the agent can use those tools.
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Why models hallucinate
Models invent plausible text when they lack a source. In AshnaAI, attach files and require quotes. Do not treat a fluent answer as a fact.
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Hybrid search for enterprise knowledge
Hybrid search mixes keywords and vectors. AshnaAI file chat is embedding retrieval. Connector search uses the provider. Use both in one agent when the job needs mail plus files.
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Few-shot prompting in real products
Few-shot means showing examples in the prompt. In AshnaAI, put examples in the agent spec or attach a sample file instead of pasting ten emails every turn.
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Orchestrating multi-agent teams
Multi-agent means more than one spec. In AshnaAI, start with one agent. Split only when two jobs have different tools or approval rules.
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No-code AI: prototype to production
AshnaAI agents are no-code: job, spec, tools, test, then embed or schedule. Production means permissions and confirms, not a rewrite in Python.
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The prompt playbook for support and ops agents
A support agent should search the source, quote it, and escalate when unsure. In AshnaAI, attach Notion, Drive, or a PDF—then add Slack or email for the reply.
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Token economics: cutting AI costs
Tokens are pieces of text you pay for. In AshnaAI, retrieve a passage instead of pasting the book, and keep X1 as the default router.
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Vector stores 101 for RAG
A vector store holds embeddings so retrieval can find similar text. AshnaAI embeds your uploaded files. You do not provision a separate Pinecone project for the how-to path.
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Automating repetitive work with AI agents
Repetitive work in AshnaAI is a narrow agent plus a schedule or embed. Connect the real tool. Do not automate a job the connector cannot do.
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Building your first AI agent
Your first agent should do one job with one or two tools. Write the job, accept the spec, test with a real example, then stop. A first agent that tries to run the company will fail in public.
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LLM routing: match tasks to models
Routing is matching a task to a catalog row. Ashna-X1 is the default. Pin GPT, Claude, or Gemini when you need that row’s context, reasoning, or file flags. Brand preference is not routing.
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Designing human-in-the-loop workflows
Put a person in front of irreversible actions. Use requestUserInput for questions, choices, files, and acknowledgements. Use Tally review for missing ledgers. Do not ask when the answer is already in the chat.
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RAG done right: ingestion and chunking
Good RAG starts before the question. Upload the file, let embeddings finish, then ask a locatable question. AshnaAI links the file to the chat first and embeds in the background. It does not train on the document.
Frequently asked questions
- What is RAG?
- Retrieval-augmented generation: attach a file, retrieve a passage, then answer. See the RAG guide.