LOCKEDIN / FORWARD-DEPLOYED ENGINEERING 101
Understand it.
Then build it.
You can be an experienced engineer and still be new to agents. Start with what each thing is and how it helps a person do useful work. Explore 48 original introductions across software, agents, cloud, security and delivery. Start here without a model key; each topic connects to deeper instruction and practice.
- 101UnderstandPlain language, visual walkthrough, vocabulary
- 102Build with guidanceOriginal lessons, examples and knowledge checks
- 103Practise and explainRunnable labs, failure cases and handover
- 01Represent
- 02Compare
- 03Verify
Numeric representations and similarity search
What are embeddings and vector search?
An embedding represents content as a list of numbers learned by a model. Vector search compares those representations to find similar items. It helps match meaning beyond exact words, but similarity is a retrieval signal, not a statement that two items are equally true or permitted.
FDEs need to diagnose retrieval quality instead of blaming every bad answer on the language model.
CONNECT IT TO WHAT YOU KNOW
Start with something familiar.
Imagine placing documents on a map where related meanings tend to be nearby. Distance suggests candidates; it does not tell you which document is authoritative.
An embedding model is different from the model that writes the answer. Changing the embedding model can require re-embedding the stored collection.
Words worth knowing
- Vector
- An ordered list of numbers.
- Chunk
- A selected segment of a larger document.
- Hybrid search
- Combining semantic and lexical retrieval signals.
ONE EXAMPLE / FICTIONAL CASE
Watch the work take shape.
A query for 'motor running hot' may find a procedure titled 'thermal overload', even though the exact words differ.
- 01Represent
- 02Compare
- 03Verify
Represent
Split content into useful passages, retain source metadata and embed them consistently.
Vectors tied to source passagesFROM RECOGNITION TO REASONING
Design three retrieval queries for one procedure.
- 01
Use an exact term, a paraphrase and an ambiguous phrase.
- 02
List the passages each should retrieve.
- 03
Include an unrelated but similar-sounding document as a negative case.
A small retrieval evaluation set with expected sources.
Compare your reasoning when you are ready
A useful evaluation checks both what should be found and what should be excluded. A high similarity score by itself does not establish the right source.
Keep notes in your own working copy. Moving through these cards does not record completion.
TURN THE IDEA INTO USEFUL WORK
You can now go deeper.
An embedding model is different from the model that writes the answer. Changing the embedding model can require re-embedding the stored collection.
Academy lessons use your existing training access. Free guides stay open.